feat(perception): split vegetation evidence layers

apps/control-station/src/core/laboratory/advancedIndex.ts

@@ -48,9 +48,13 @@ import { fetchM48SFixedClassDetectorResult } from "./m48sFixedClassDetector";
 import { fetchM48TRiskQualityResult } from "./m48tRiskQuality";
 import { fetchM49TgsFailClosedResult } from "./m49TgsFailClosed";
 import { fetchM49TgsFullShadowResult } from "./m49TgsFullShadow";
-import { fetchVegetationShadowResult } from "./vegetationShadow";
+import {
+  fetchVegetationBenchmarkResult,
+  fetchVegetationShadowResult,
+} from "./vegetationShadow";
 
 export type AdvancedLaboratoryWorkId =
+  | "lab-v1-vegetation-benchmark"
   | "lab-v1-vegetation-shadow"
   | "m48-object-centric-quality"
   | "m48-small-static-passage-regression"
@@ -102,6 +106,7 @@ export interface AdvancedLaboratoryIndexItem {
 }
 
 const WORK_IDS: readonly AdvancedLaboratoryWorkId[] = [
+  "lab-v1-vegetation-benchmark",
   "lab-v1-vegetation-shadow",
   "m48-object-centric-quality",
   "m48-small-static-passage-regression",
@@ -148,6 +153,7 @@ const WORK_IDS: readonly AdvancedLaboratoryWorkId[] = [
 ];
 
 const RESULT_PREFIX: Readonly<Record<AdvancedLaboratoryWorkId, string>> = {
+  "lab-v1-vegetation-benchmark": "lab-v1-vegetation-benchmark",
   "lab-v1-vegetation-shadow": "lab-v1-vegetation-shadow",
   "m48-object-centric-quality": "m48-object-quality-(?:pack|result)",
   "m48-small-static-passage-regression": "m48-small-static-passage-regression",
@@ -201,6 +207,7 @@ export function isAdvancedLaboratoryWorkId(
 
 export function emptyAdvancedLaboratoryResults(): AdvancedLaboratoryResults {
   return {
+    vegetationBenchmark: null,
     vegetationShadow: null,
     m47Graph: null,
     m48: null,
@@ -335,7 +342,8 @@ export function advancedLaboratoryResultAvailable(
   workId: AdvancedLaboratoryWorkId,
   results: AdvancedLaboratoryResults,
 ): boolean {
-  return workId === "lab-v1-vegetation-shadow" ? results.vegetationShadow !== null
+  return workId === "lab-v1-vegetation-benchmark" ? results.vegetationBenchmark !== null
+    : workId === "lab-v1-vegetation-shadow" ? results.vegetationShadow !== null
     : workId === "m48-object-centric-quality" ? results.m48 !== null
     : workId === "m48-small-static-passage-regression" ? results.m48SmallStatic !== null
     : workId === "m48-static-occupancy-qualification" ? results.m48StaticOccupancy !== null
@@ -393,7 +401,10 @@ export async function fetchAdvancedLaboratoryResult(
   } = {},
 ): Promise<AdvancedLaboratoryResults> {
   const results = emptyAdvancedLaboratoryResults();
-  if (workId === "lab-v1-vegetation-shadow") {
+  if (workId === "lab-v1-vegetation-benchmark") {
+    if (!resultId) throw new AdvancedLaboratoryContractError("Vegetation benchmark identity не выбрана.");
+    results.vegetationBenchmark = await fetchVegetationBenchmarkResult(resultId, { fetcher, signal });
+  } else if (workId === "lab-v1-vegetation-shadow") {
     if (!resultId) throw new AdvancedLaboratoryContractError("Vegetation LAB identity не выбрана.");
     results.vegetationShadow = await fetchVegetationShadowResult(resultId, { fetcher, signal });
   } else if (workId === "m48-object-centric-quality") {

apps/control-station/src/core/laboratory/advancedLaboratoryResults.ts

@@ -45,6 +45,7 @@ import type { M49TgsFullShadowResult } from "./m49TgsFullShadow";
 import type { VegetationShadowResult } from "./vegetationShadow";
 
 export interface AdvancedLaboratoryResults {
+  vegetationBenchmark: VegetationShadowResult | null;
   vegetationShadow: VegetationShadowResult | null;
   m47Graph: M47ReferenceGraphLabResult | null;
   m48: M48AdvancedResult | null;

apps/control-station/src/core/laboratory/advancedResults.ts

@@ -967,6 +967,7 @@ export async function fetchAdvancedLaboratoryResults({
   const e39 = settledCatalogValue(settled[7]);
   const e40 = settledCatalogValue(settled[8]);
   return {
+    vegetationBenchmark: null,
     vegetationShadow: null,
     m47Graph: null, m48: null, m48SmallStatic: null, m48StaticOccupancy: null,
     m48r3StaticOccupancy: null,

apps/control-station/src/core/laboratory/vegetationShadow.ts

@@ -1,6 +1,7 @@
 import type { LaboratoryFetch } from "./advancedResults";
 
 const RESULT_ID = /^lab-v1-vegetation-shadow-[a-f0-9]{64}$/;
+const BENCHMARK_RESULT_ID = /^lab-v1-vegetation-benchmark-[a-f0-9]{64}$/;
 const SHA256 = /^[a-f0-9]{64}$/;
 const CANDIDATES = ["ddrnet", "ppliteseg"] as const;
 const ROUTE_MODES = ["source", "ddrnet", "ppliteseg", "urban", "rural", "offroad"] as const;
@@ -75,6 +76,7 @@ export interface VegetationRouteVideo {
   aggregatePredictionPixels: readonly number[];
   policyPresets: Readonly<Record<string, Readonly<Record<string, string>>>> | null;
   fusionMode: "synchronised-multilayer-review" | null;
+  validFovMaskSha256: string | null;
 }
 
 export interface VegetationShadowResult {
@@ -192,6 +194,7 @@ function visualCaseValue(
   value: unknown,
   resultId: string,
   expectedKind: "goose" | "ravnoves",
+  endpointRoot: string,
 ): VegetationVisualCase {
   const row = objectValue(value, `vegetation.${expectedKind}.case`);
   exact(row.source_kind, expectedKind, "vegetation.case.source_kind");
@@ -210,7 +213,7 @@ function visualCaseValue(
     if (!SHA256.test(sha256) || !path.startsWith(`visual/${expectedKind}/${caseId}/`)) {
       throw new VegetationShadowContractError(`vegetation.case.assets.${key}: proof invalid.`);
     }
-    projected[key] = `/api/v1/laboratory/vegetation-shadow/${encodeURIComponent(resultId)}/assets/${path
+    projected[key] = `${endpointRoot}/${encodeURIComponent(resultId)}/assets/${path
       .split("/")
       .map(encodeURIComponent)
       .join("/")}`;
@@ -342,10 +345,16 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
         evidenceState,
       };
     });
-  const expectedClassCount = viewKind === "coarse-material-policy-review" ? 9 : 64;
+  const expectedClassCount = viewKind === "coarse-material-policy-review" ? 10 : 64;
   if (classes.length !== expectedClassCount) {
     throw new VegetationShadowContractError("vegetation.route_video: taxonomy size changed.");
   }
+  if (
+    viewKind === "coarse-material-policy-review"
+    && (classes[9]?.disposition !== "undefined" || classes[9]?.evidenceState !== "UNOBSERVED")
+  ) {
+    throw new VegetationShadowContractError("vegetation.route_video: valid-FOV class changed.");
+  }
   const aggregatePredictionPixels = arrayValue(
     row.aggregate_prediction_pixels,
     "vegetation.route_video.aggregate_prediction_pixels",
@@ -364,7 +373,18 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
   integerValue(maskArchive.byte_length, "vegetation.route_video.mask_archive.byte_length");
   let policyPresets: VegetationRouteVideo["policyPresets"] = null;
   let fusionMode: VegetationRouteVideo["fusionMode"] = null;
+  let validFovMaskSha256: string | null = null;
   if (viewKind === "coarse-material-policy-review") {
+    const validFov = objectValue(row.valid_fov, "vegetation.route_video.valid_fov");
+    exact(validFov.mask_path, "video/valid-fov-mask.png", "vegetation.route_video.valid_fov.path");
+    validFovMaskSha256 = textValue(
+      validFov.mask_sha256,
+      "vegetation.route_video.valid_fov.sha256",
+    );
+    if (!SHA256.test(validFovMaskSha256)) {
+      throw new VegetationShadowContractError("vegetation.route_video: valid-FOV digest invalid.");
+    }
+    exact(validFov.outside_valid_fov_class_id, 9, "vegetation.route_video.valid_fov.class_id");
     const policy = objectValue(row.policy, "vegetation.route_video.policy");
     const presets = objectValue(policy.presets, "vegetation.route_video.policy.presets");
     policyPresets = Object.fromEntries(Object.entries(presets).map(([presetId, rawRules]) => {
@@ -399,10 +419,15 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
     aggregatePredictionPixels,
     policyPresets,
     fusionMode,
+    validFovMaskSha256,
   };
 }
 
-function parseResult(value: unknown, resultId: string): VegetationShadowResult {
+function parseResult(
+  value: unknown,
+  resultId: string,
+  endpointRoot: string,
+): VegetationShadowResult {
   const payload = objectValue(value, "Vegetation LAB");
   exact(payload.schema_version, "missioncore.lab-v1-vegetation-shadow/v1", "vegetation.schema");
   exact(payload.result_id, resultId, "vegetation.result_id");
@@ -438,9 +463,9 @@ function parseResult(value: unknown, resultId: string): VegetationShadowResult {
     "vegetation.authority.camera_semantics_can_clear_rigid_geometry",
   );
   const routeCases = arrayValue(catalogs.ravnoves, "vegetation.catalogs.ravnoves")
-    .map((item) => visualCaseValue(item, resultId, "ravnoves"));
+    .map((item) => visualCaseValue(item, resultId, "ravnoves", endpointRoot));
   const validationCases = arrayValue(catalogs.goose, "vegetation.catalogs.goose")
-    .map((item) => visualCaseValue(item, resultId, "goose"));
+    .map((item) => visualCaseValue(item, resultId, "goose", endpointRoot));
   if (routeCases.length !== 0 || validationCases.length !== 12) {
     throw new VegetationShadowContractError("vegetation.catalogs: ожидалось 12 truth-backed GOOSE случаев без route viewer.");
   }
@@ -490,5 +515,36 @@ export async function fetchVegetationShadowResult(
   if (!response.ok) {
     throw new VegetationShadowContractError(`Vegetation LAB недоступна: HTTP ${response.status}.`);
   }
-  return parseResult(await response.json(), resultId);
+  return parseResult(
+    await response.json(),
+    resultId,
+    "/api/v1/laboratory/vegetation-shadow",
+  );
+}
+
+export async function fetchVegetationBenchmarkResult(
+  resultId: string,
+  {
+    fetcher = fetch,
+    signal,
+  }: { fetcher?: LaboratoryFetch; signal?: AbortSignal } = {},
+): Promise<VegetationShadowResult> {
+  if (!BENCHMARK_RESULT_ID.test(resultId)) {
+    throw new VegetationShadowContractError("Vegetation benchmark identity недопустима.");
+  }
+  const endpointRoot = "/api/v1/laboratory/vegetation-benchmark";
+  const response = await fetcher(
+    `${endpointRoot}/${encodeURIComponent(resultId)}`,
+    { method: "GET", headers: { Accept: "application/json" }, signal },
+  );
+  if (!response.ok) {
+    throw new VegetationShadowContractError(
+      `Vegetation benchmark недоступен: HTTP ${response.status}.`,
+    );
+  }
+  const result = parseResult(await response.json(), resultId, endpointRoot);
+  if (result.routeVideo) {
+    throw new VegetationShadowContractError("Vegetation benchmark содержит route video.");
+  }
+  return result;
 }

apps/control-station/src/styles/m4-replay-threat.css

@@ -81,6 +81,21 @@
   justify-content: flex-end;
 }
 
+.m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"][data-multi-semantic="true"] {
+  flex-wrap: wrap;
+}
+
+.m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"][data-multi-semantic="true"]
+  > .m4-replay-threat-visual__pane-layer-controls {
+  flex: 1 0 100%;
+  justify-content: flex-start;
+}
+
+.m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"][data-multi-semantic="true"]
+  > .m4-replay-threat-visual__pane-mode-controls {
+  margin-left: auto;
+}
+
 .m4-replay-threat-evidence-viewer[data-mode-controls="content"]:has(
   .m4-replay-threat-visual__review-controls
 ) .m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"] {

apps/control-station/src/workspaces/laboratory/AdvancedLaboratoryResult.tsx

@@ -51,6 +51,7 @@ import { M48TRiskQualityResultView } from "./M48TRiskQualityResult";
 import { M49TgsFailClosedResultView } from "./M49TgsFailClosedResult";
 import { M49TgsFullShadowResultView } from "./M49TgsFullShadowResult";
 import { VegetationShadowResultView } from "./VegetationShadowResult";
+import { VegetationBenchmarkResultView } from "./VegetationBenchmarkResult";
 
 export { isAdvancedLaboratoryWorkId };
 export type { AdvancedLaboratoryWorkId };
@@ -93,6 +94,9 @@ export function AdvancedLaboratoryResult({
   failedSessionId: string | null;
   replayError: string | null;
 }) {
+  if (workId === "lab-v1-vegetation-benchmark" && results.vegetationBenchmark) {
+    return <VegetationBenchmarkResultView rigLabel={rigLabel} result={results.vegetationBenchmark} />;
+  }
   if (workId === "lab-v1-vegetation-shadow" && results.vegetationShadow) {
     return <VegetationShadowResultView rigLabel={rigLabel} result={results.vegetationShadow} />;
   }

apps/control-station/src/workspaces/laboratory/M49TgsFullShadowEvidence.tsx

@@ -71,7 +71,6 @@ export function M49TgsFullShadowEvidence({
     const controller = new AbortController();
     setSemantic(null);
     setSemanticError(null);
-    if (semanticOverride) return () => controller.abort();
     void fetchE47SemanticSlamResult({
       resultId: result.source.linkedSemanticResultId,
       signal: controller.signal,
@@ -87,7 +86,7 @@ export function M49TgsFullShadowEvidence({
         if (!controller.signal.aborted) setSemanticError(message(caught));
       });
     return () => controller.abort();
-  }, [result.source.linkedSemanticResultId, result.source.linkedVisualResultId, semanticOverride]);
+  }, [result.source.linkedSemanticResultId, result.source.linkedVisualResultId]);
 
   useEffect(() => {
     const controller = new AbortController();
@@ -201,15 +200,28 @@ export function M49TgsFullShadowEvidence({
   const handleSequenceChange = useCallback((sequence: number | null) => {
     setActiveSequence(sequence);
   }, []);
+  const semanticLayers = useMemo<readonly M4ReplayThreatSemanticLayer[]>(() => [
+    ...(semantic ? [{
+      id: "urban",
+      controlLabel: "ГОРОД · EoMT",
+      resultId: semantic.resultId,
+      taxonomy: semantic.taxonomy,
+      label: "EoMT Cityscapes semantic · recorded video",
+      maskAriaLabel: "EoMT urban semantic prediction",
+    }] : []),
+    ...(semanticOverride ? [{
+      ...semanticOverride,
+      id: semanticOverride.id ?? "vegetation",
+      controlLabel: semanticOverride.controlLabel ?? "ПРИРОДА · DDRNet",
+    }] : []),
+  ], [semantic, semanticOverride]);
 
   return (
     <>
       <M4ReplayThreatVisual
         resultId={result.source.linkedVisualResultId}
-        semantic={semanticOverride ?? (semantic ? {
+        semanticLayers={semanticLayers}
-          resultId: semantic.resultId,
+        initialSemanticLayerId={semanticOverride ? "vegetation" : "urban"}
-          taxonomy: semantic.taxonomy,
-        } : undefined)}
         showReviewAnchorBoxes={false}
         reviewLabel="4 489 source-paced TGS frames"
         evidenceLabel={evidenceLabel}
@@ -227,7 +239,7 @@ export function M49TgsFullShadowEvidence({
           replacePointCloud: false,
         }}
       />
-      {!semanticOverride && semanticError ? (
+      {semanticError ? (
         <div className="m4-replay-threat-visual__pane-status" role="alert">
           Semantic overlay недоступен: {semanticError}
         </div>

apps/control-station/src/workspaces/laboratory/M4ReplayThreatVisual.tsx

@@ -96,6 +96,8 @@ function SpatialState({ message: text }: { message: string }) {
 }
 
 export interface M4ReplayThreatSemanticLayer {
+  id?: string;
+  controlLabel?: string;
   resultId: string;
   spatialResultId?: string | null;
   maskUrl?: (sequence: number) => string;
@@ -155,6 +157,8 @@ const EMPTY_REVIEW_ANCHORS: readonly M4ReplayThreatReviewAnchor[] = [];
 export function M4ReplayThreatVisual({
   resultId,
   semantic,
+  semanticLayers,
+  initialSemanticLayerId,
   reviewAnchors = EMPTY_REVIEW_ANCHORS,
   showReviewAnchorBoxes = true,
   reviewLabel = "Контрольные примеры M4.8R1",
@@ -168,6 +172,8 @@ export function M4ReplayThreatVisual({
 }: {
   resultId: string;
   semantic?: M4ReplayThreatSemanticLayer;
+  semanticLayers?: readonly M4ReplayThreatSemanticLayer[];
+  initialSemanticLayerId?: string;
   reviewAnchors?: readonly M4ReplayThreatReviewAnchor[];
   showReviewAnchorBoxes?: boolean;
   reviewLabel?: string;
@@ -199,6 +205,35 @@ export function M4ReplayThreatVisual({
   ));
   const [expanded, setExpanded] = useState(false);
   const [selectedReviewAnchorIndex, setSelectedReviewAnchorIndex] = useState(0);
+  const availableSemanticLayers = useMemo<readonly M4ReplayThreatSemanticLayer[]>(
+    () => semanticLayers?.length ? semanticLayers : semantic ? [semantic] : [],
+    [semantic, semanticLayers],
+  );
+  const semanticLayerIdentity = availableSemanticLayers
+    .map((layer, index) => layer.id ?? `${layer.resultId}:${index}`)
+    .join("|");
+  const [selectedSemanticLayerId, setSelectedSemanticLayerId] = useState(
+    initialSemanticLayerId ?? "",
+  );
+  useEffect(() => {
+    if (!availableSemanticLayers.length) {
+      setSelectedSemanticLayerId("");
+      return;
+    }
+    const selectedStillExists = availableSemanticLayers.some(
+      (layer, index) => (layer.id ?? `${layer.resultId}:${index}`) === selectedSemanticLayerId,
+    );
+    if (selectedStillExists) return;
+    const preferred = initialSemanticLayerId
+      ? availableSemanticLayers.find((layer) => layer.id === initialSemanticLayerId)
+      : null;
+    const next = preferred ?? availableSemanticLayers[0]!;
+    const nextIndex = availableSemanticLayers.indexOf(next);
+    setSelectedSemanticLayerId(next.id ?? `${next.resultId}:${nextIndex}`);
+  }, [availableSemanticLayers, initialSemanticLayerId, semanticLayerIdentity, selectedSemanticLayerId]);
+  const activeSemantic = availableSemanticLayers.find(
+    (layer, index) => (layer.id ?? `${layer.resultId}:${index}`) === selectedSemanticLayerId,
+  ) ?? availableSemanticLayers[0];
   const metricSceneRef = useRef<LaboratoryMetricEvidenceSceneHandle | null>(null);
   const metadata = useM4ThreatTimelineMetadata(resultId, timelineEndpointRoot);
   const playbackRange = useMemo(() => metadata.timeline ? ({
@@ -298,19 +333,21 @@ export function M4ReplayThreatVisual({
     sequence: frame?.sequence ?? null,
     endpointRoot: timelineEndpointRoot,
   });
-  const semanticSpatialResultId = semantic
+  const semanticSpatialResultId = activeSemantic
-    ? semantic.spatialResultId === undefined ? semantic.resultId : semantic.spatialResultId
+    ? activeSemantic.spatialResultId === undefined
+      ? activeSemantic.resultId
+      : activeSemantic.spatialResultId
     : null;
   const spatialSemanticTaxonomy = useMemo<readonly E47SemanticClass[]>(
-    () => semanticSpatialResultId && semantic
+    () => semanticSpatialResultId && activeSemantic
-      ? semantic.taxonomy.map((item) => ({
+      ? activeSemantic.taxonomy.map((item) => ({
           classId: item.classId,
           label: item.label,
           disposition: item.disposition === "ambiguous" ? "ambiguous" : "labeled",
           colorRgb: item.colorRgb,
         }))
       : [],
-    [semantic, semanticSpatialResultId],
+    [activeSemantic, semanticSpatialResultId],
   );
   const semanticTimeline = useE47SemanticTimelineFrame({
     resultId: semanticSpatialResultId,
@@ -386,14 +423,14 @@ export function M4ReplayThreatVisual({
     [frame, reviewAnchorBoxes, showReferenceMediaLayers, staticObstacleBoxes],
   );
   const semanticClasses = useMemo<readonly RecordedEvidenceSemanticClass[]>(
-    () => semantic?.taxonomy.map((item) => ({
+    () => activeSemantic?.taxonomy.map((item) => ({
       id: item.classId,
       label: `semantic: ${item.label}`,
     })) ?? [],
-    [semantic?.taxonomy],
+    [activeSemantic?.taxonomy],
   );
   const semanticPalette = useMemo<readonly RecordedEvidenceSemanticPaletteEntry[]>(
-    () => semantic?.taxonomy.map((item) => ({
+    () => activeSemantic?.taxonomy.map((item) => ({
       classId: item.classId,
       color: item.disposition === "undefined"
         ? { kind: "transparent" as const }
@@ -404,7 +441,7 @@ export function M4ReplayThreatVisual({
         ? 0
         : item.disposition === "ambiguous" ? 0.52 : 0.92,
     })) ?? [],
-    [semantic?.taxonomy],
+    [activeSemantic?.taxonomy],
   );
   const semanticFrame = semanticTimeline.activeFrame?.sequence === frame?.sequence
     ? semanticTimeline.activeFrame
@@ -422,7 +459,7 @@ export function M4ReplayThreatVisual({
       && lastSpatialSemanticFrameRef.current.frame.sequence === spatialFrame?.sequence
       ? lastSpatialSemanticFrameRef.current.frame
       : null;
-  const semanticIntegrityError = semantic && spatialFrame && spatialSemanticFrame && (
+  const semanticIntegrityError = activeSemantic && spatialFrame && spatialSemanticFrame && (
     spatialSemanticFrame.sourcePointCount !== spatialFrame.pointCloudSourceCount
     || spatialFrame.pointCloudSampleCount !== spatialFrame.pointCloudSourceCount
     || spatialFrame.pointCloudBodyXyzM.length !== spatialFrame.pointCloudSourceCount
@@ -431,7 +468,7 @@ export function M4ReplayThreatVisual({
     : null;
   const alignedSemanticPointIds = useMemo<readonly (number | null)[] | undefined>(() => {
     if (
-      !semantic
+      !activeSemantic
       || !showSpatialSemantic
       || !spatialFrame
       || !spatialSemanticFrame
@@ -441,7 +478,7 @@ export function M4ReplayThreatVisual({
       const status = spatialSemanticFrame.statusCodes[index];
       return status === 2 || status === 3 ? classId : null;
     });
-  }, [semantic, semanticIntegrityError, showSpatialSemantic, spatialFrame, spatialSemanticFrame]);
+  }, [activeSemantic, semanticIntegrityError, showSpatialSemantic, spatialFrame, spatialSemanticFrame]);
   const activeSpatialFrame = spatialFrame?.sequence === timelineFrame.activeSequence
     ? spatialFrame
     : null;
@@ -610,19 +647,19 @@ export function M4ReplayThreatVisual({
     },
   ), [metadata.timeline, spatialFrame, timelineFrame.availableFrames]);
   const semanticOverlay: RecordedEvidenceSemanticOverlay | undefined =
-    semantic && showMediaSemantic && frame
+    activeSemantic && showMediaSemantic && frame
       ? {
-          src: semantic.maskUrl?.(frame.sequence)
+          src: activeSemantic.maskUrl?.(frame.sequence)
-            ?? e47SemanticMaskUrl(semantic.resultId, frame.sequence),
+            ?? e47SemanticMaskUrl(activeSemantic.resultId, frame.sequence),
           prefetchSrcs: Array.from({ length: 12 }, (_, index) => index + 1)
             .map((offset) => frame.sequence + offset)
             .filter((sequence) => sequence < (metadata.timeline?.frameCount ?? 0))
-            .map((sequence) => semantic.maskUrl?.(sequence)
+            .map((sequence) => activeSemantic.maskUrl?.(sequence)
-              ?? e47SemanticMaskUrl(semantic.resultId, sequence)),
+              ?? e47SemanticMaskUrl(activeSemantic.resultId, sequence)),
           classes: semanticClasses,
           palette: semanticPalette,
           opacity: 0.9,
-          ariaLabel: `${semantic.maskAriaLabel ?? "Semantic prediction"} frame ${frame.sequence + 1}`,
+          ariaLabel: `${activeSemantic.maskAriaLabel ?? "Semantic prediction"} frame ${frame.sequence + 1}`,
         }
       : undefined;
   const accumulatedCameraPoints = cameraPointOverlay.overlay?.sequence === frame?.sequence
@@ -692,7 +729,7 @@ export function M4ReplayThreatVisual({
     </div>
   );
 
-  const mediaLayerControls = semantic
+  const mediaLayerControls = activeSemantic
     || (showReferenceMediaLayers && metadata.timeline?.cameraPointDelivery)
     || (showReferenceMediaLayers && metadata.timeline?.cameraObstacleProjectionDelivery) ? (
     <div
@@ -700,7 +737,7 @@ export function M4ReplayThreatVisual({
       role="group"
       aria-label="Слои камеры и видео"
     >
-      {semantic ? (
+      {activeSemantic ? (
         <Button
           size="compact"
           shape="pill"
@@ -711,6 +748,20 @@ export function M4ReplayThreatVisual({
           SEMANTICS
         </Button>
       ) : null}
+      {availableSemanticLayers.length > 1 ? (
+        <SegmentedControl
+          value={selectedSemanticLayerId}
+          items={availableSemanticLayers.map((layer, index) => ({
+            value: layer.id ?? `${layer.resultId}:${index}`,
+            label: layer.controlLabel ?? layer.label ?? `SEMANTIC ${index + 1}`,
+          }))}
+          label="Источник семантики"
+          onChange={(value) => {
+            setSelectedSemanticLayerId(value);
+            setShowMediaSemantic(true);
+          }}
+        />
+      ) : null}
       {showReferenceMediaLayers && metadata.timeline?.cameraPointDelivery ? (
         <Button
           size="compact"
@@ -1022,6 +1073,7 @@ export function M4ReplayThreatVisual({
           <div
             className="m4-replay-threat-visual__pane-toolbar"
             data-pane-toolbar="media"
+            data-multi-semantic={availableSemanticLayers.length > 1 ? "true" : undefined}
           >
             {mediaLayerControls}
             {mediaModeControls}
@@ -1238,8 +1290,8 @@ export function M4ReplayThreatVisual({
   return (
     <div className="l3-visual-audit m4-replay-threat-visual">
       <LaboratoryEvidenceViewer
-        label={semantic
+        label={activeSemantic
-          ? semantic.label ?? "Semantic diagnostic replay"
+          ? activeSemantic.label ?? "Semantic diagnostic replay"
           : `${evidenceLabel} recorded-realtime replay`}
         className="m4-replay-threat-evidence-viewer"
         mode={mediaMode ?? "none"}

apps/control-station/src/workspaces/laboratory/VegetationBenchmarkResult.tsx

@@ -0,0 +1,147 @@
+import {
+  LaboratoryEvidence,
+  LaboratoryResultSummary,
+  LaboratorySummary,
+  LaboratoryWorkTemplate,
+} from "../../components/laboratory/LaboratoryPresentation";
+import type { VegetationShadowResult } from "../../core/laboratory/vegetationShadow";
+import {
+  M48MaskComparisonVisual,
+  type M48MaskComparisonCase,
+} from "./M48FailureAtlasVisual";
+
+function decimal(value: number, digits = 1): string {
+  return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
+}
+
+const VEGETATION_LABELS: Readonly<Record<string, string>> = {
+  high_grass: "Высокая трава",
+  low_grass: "Низкая трава",
+  bush: "Куст",
+  tree_trunk: "Ствол дерева",
+  tree_crown: "Крона дерева",
+  hedge: "Живая изгородь",
+  forest: "Лесная растительность",
+  crops: "Посевы",
+};
+
+function comparisonCases(result: VegetationShadowResult): readonly M48MaskComparisonCase[] {
+  return result.validationCases.map((item) => {
+    const focus = item.focus!;
+    return {
+      caseId: item.caseId,
+      title: `${VEGETATION_LABELS[focus.className] ?? focus.className} · truth ${decimal(focus.truthFraction * 100, 1)}% кадра`,
+      sourceUrl: item.assets.source,
+      truthUrl: item.assets.truth,
+      predictions: {
+        ddrnet: item.assets.ddrnet,
+        ppliteseg: item.assets.ppliteseg,
+      },
+      errors: {
+        ddrnet: item.assets.ddrnet_error,
+        ppliteseg: item.assets.ppliteseg_error,
+      },
+    };
+  });
+}
+
+export function VegetationBenchmarkResultView({
+  rigLabel,
+  result,
+}: {
+  rigLabel: string;
+  result: VegetationShadowResult;
+}) {
+  const selected = result.candidates.find(
+    (candidate) => candidate.candidate === result.selectedCandidate,
+  )!;
+  const alternative = result.candidates.find(
+    (candidate) => candidate.candidate !== result.selectedCandidate,
+  )!;
+
+  return (
+    <LaboratoryWorkTemplate
+      summary={(
+        <LaboratorySummary
+          title="M4.8 · архивный benchmark растительности"
+          description="Отдельный truth-backed контур GOOSE для сравнения готовых fine-64 весов. Он не является частью RAVNOVES00 realtime LAB и открывается автономно без Worker 006."
+          status="ARCHIVE ANALYSIS · model qualification only · commands OFF"
+          statusTone="warning"
+          facts={[
+            { label: "Источник", value: "GOOSE validation · 962 размеченных кадра · 12 hard cases" },
+            { label: "Сравнение", value: "DDRNet-39 vs PPLiteSeg · official fine-64 weights" },
+            { label: "Кейсы", value: "трава · куст · ствол · крона · изгородь · лес · посевы" },
+            { label: "Authority", value: `${rigLabel} · MODEL QUALIFICATION ONLY · commands OFF` },
+          ]}
+          brief={{
+            question: "Какие готовые веса лучше различают проезжаемую траву, кусты и стволы на размеченных off-road кадрах?",
+            approach: "Обе модели прогнаны на 962 кадрах, а 12 визуальных кейсов выбраны детерминированно по truth-поддержке восьми растительных классов. Viewer показывает source, ручной truth, prediction и error.",
+            principalResult: `${selected.loadedModelName} лидирует по vegetation IoU: ${decimal(selected.vegetationMeanIouPercent, 2)}% против ${decimal(alternative.vegetationMeanIouPercent, 2)}%.`,
+            limitation: "GOOSE — внешний размеченный домен. Результат выбирает стартовые веса, но не доказывает качество на fisheye RAVNOVES00 и не даёт navigation authority.",
+          }}
+          method={{
+            completeness: "complete",
+            executionClass: "ai-inference",
+            pipelineId: "goose-fine64-ready-weights-benchmark-archive/v1",
+            components: result.candidates.map((candidate) => ({
+              kind: "model" as const,
+              name: candidate.loadedModelName,
+              version: candidate.candidate,
+              role: candidate.candidate === result.selectedCandidate
+                ? "selected vegetation candidate"
+                : "comparison candidate",
+              identitySha256: candidate.checkpointSha256,
+            })),
+          }}
+        />
+      )}
+      evidence={(
+        <LaboratoryEvidence
+          eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
+          title="TRUTH — ручная разметка · PREDICTION — ответ модели · ERROR — расхождение"
+          kind="diagnostic-model"
+          resizable
+        >
+          <M48MaskComparisonVisual
+            cases={comparisonCases(result)}
+            initialCandidate={result.selectedCandidate}
+          />
+        </LaboratoryEvidence>
+      )}
+      result={(
+        <LaboratoryResultSummary
+          title="DDRNet выбран как стартовый vegetation candidate"
+          status={`${selected.loadedModelName} · перенос на ровер не доказан`}
+          statusTone="warning"
+          metrics={[
+            {
+              label: "GOOSE mIoU",
+              value: `${decimal(selected.meanIouPercent, 2)}% / ${decimal(alternative.meanIouPercent, 2)}%`,
+              hint: `${selected.candidate} / ${alternative.candidate} · полный validation split`,
+            },
+            {
+              label: "Vegetation IoU",
+              value: `${decimal(selected.vegetationMeanIouPercent, 2)}% / ${decimal(alternative.vegetationMeanIouPercent, 2)}%`,
+              hint: "grass/vegetation/bush/tree и родственные fine-64 labels",
+            },
+            {
+              label: "Worker shadow p95",
+              value: `${decimal(selected.shadowLatencyP95Ms, 2)} / ${decimal(alternative.shadowLatencyP95Ms, 2)} ms`,
+              hint: "чистый inference · одна тяжёлая модель за раз",
+            },
+            {
+              label: "Peak VRAM",
+              value: `${decimal(selected.peakReservedVramBytes / 1024 ** 3, 2)} / ${decimal(alternative.peakReservedVramBytes / 1024 ** 3, 2)} GiB`,
+              hint: `${selected.candidate} / ${alternative.candidate} · RTX 4090`,
+            },
+          ]}
+          conclusion={{
+            proved: "Обе готовые fine-64 модели воспроизводимо запускаются; DDRNet лучше по aggregate vegetation IoU.",
+            notProved: "Не доказаны accuracy на нашем fisheye, temporal stability, collision safety и физическое поведение ровера.",
+            decision: "Хранить как архив квалификации весов. Проверку на RAVNOVES00 вести только в основной многослойной LAB.",
+          }}
+        />
+      )}
+    />
+  );
+}

apps/control-station/src/workspaces/laboratory/VegetationShadowResult.tsx

@@ -14,10 +14,6 @@ import {
   fetchM49TgsFullShadowResult,
   type M49TgsFullShadowResult,
 } from "../../core/laboratory/m49TgsFullShadow";
-import {
-  M48MaskComparisonVisual,
-  type M48MaskComparisonCase,
-} from "./M48FailureAtlasVisual";
 import { M4ReplayThreatVisual } from "./M4ReplayThreatVisual";
 import { M49TgsFullShadowEvidence } from "./M49TgsFullShadowEvidence";
 
@@ -25,37 +21,6 @@ function decimal(value: number, digits = 1): string {
   return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
 }
 
-const VEGETATION_LABELS: Readonly<Record<string, string>> = {
-  high_grass: "Высокая трава",
-  low_grass: "Низкая трава",
-  bush: "Куст",
-  tree_trunk: "Ствол дерева",
-  tree_crown: "Крона дерева",
-  hedge: "Живая изгородь",
-  forest: "Лесная растительность",
-  crops: "Посевы",
-};
-
-function comparisonCases(result: VegetationShadowResult): readonly M48MaskComparisonCase[] {
-  return result.validationCases.map((item) => {
-    const focus = item.focus!;
-    return {
-      caseId: item.caseId,
-      title: `${VEGETATION_LABELS[focus.className] ?? focus.className} · truth ${decimal(focus.truthFraction * 100, 1)}% кадра`,
-      sourceUrl: item.assets.source,
-      truthUrl: item.assets.truth,
-      predictions: {
-        ddrnet: item.assets.ddrnet,
-        ppliteseg: item.assets.ppliteseg,
-      },
-      errors: {
-        ddrnet: item.assets.ddrnet_error,
-        ppliteseg: item.assets.ppliteseg_error,
-      },
-    };
-  });
-}
-
 function VegetationRouteEvidence({ result }: { result: VegetationShadowResult }) {
   const route = result.routeVideo!;
   const [tgs, setTgs] = useState<M49TgsFullShadowResult | null>(null);
@@ -83,16 +48,14 @@ function VegetationRouteEvidence({ result }: { result: VegetationShadowResult })
   }, [route.baseM4ResultId, route.linkedTgsResultId]);
 
   const semantic = {
+    id: "vegetation",
+    controlLabel: "ПРИРОДА · DDRNet",
     resultId: route.workerResultId,
     spatialResultId: null,
     taxonomy: route.taxonomy,
     maskUrl: (sequence: number) => vegetationVideoMaskUrl(result.resultId, sequence),
-    label: route.viewKind === "coarse-material-policy-review"
+    label: "DDRNet coarse vegetation material · recorded video",
-      ? "Coarse material evidence · recorded video"
+    maskAriaLabel: "DDRNet vegetation material prediction",
-      : "DDRNet vegetation prediction · recorded video",
-    maskAriaLabel: route.viewKind === "coarse-material-policy-review"
-      ? "Coarse material policy evidence"
-      : "DDRNet vegetation prediction",
   } as const;
 
   if (route.linkedTgsResultId && tgs) {
@@ -100,19 +63,23 @@ function VegetationRouteEvidence({ result }: { result: VegetationShadowResult })
       <M49TgsFullShadowEvidence
         result={tgs}
         semanticOverride={semantic}
-        evidenceLabel="LAB V1 · MATERIAL + YOLOX + TGS"
+        evidenceLabel="LAB V1 · EoMT + DDRNet + YOLOX + TGS"
       />
     );
   }
   if (route.linkedTgsResultId && !tgsError) {
-    return <div className="m4-replay-threat-visual__pane-status" role="status">Открываем sealed TGS и coarse material timeline…</div>;
+    return (
+      <div className="m4-replay-threat-visual__pane-status" role="status">
+        Открываем sealed EoMT, TGS и coarse vegetation timeline…
+      </div>
+    );
   }
   return (
     <>
       <M4ReplayThreatVisual
         resultId={route.baseM4ResultId}
         evidenceLabel="LAB V1 · DDRNet"
-        showReferenceMediaLayers={route.viewKind === "coarse-material-policy-review"}
+        showReferenceMediaLayers
         showSpatialOverlaySummary={false}
         semantic={semantic}
       />
@@ -132,146 +99,117 @@ export function VegetationShadowResultView({
   rigLabel: string;
   result: VegetationShadowResult;
 }) {
+  const route = result.routeVideo;
   const selected = result.candidates.find(
     (candidate) => candidate.candidate === result.selectedCandidate,
   )!;
-  const alternative = result.candidates.find(
+
-    (candidate) => candidate.candidate !== result.selectedCandidate,
-  )!;
   return (
     <LaboratoryWorkTemplate
       summary={(
         <LaboratorySummary
-          title="LAB V1 · готовые модели растительности"
+          title="LAB V1 · карта ровера · город + растительность"
-          description={result.routeVideo
+          description="Один recorded-контур RAVNOVES00 синхронно показывает городской EoMT, природный DDRNet, frozen YOLOX detections и causal TGS. Семантические маски переключаются, чтобы их цвета не скрывали друг друга; геометрическое veto остаётся независимым."
-            ? result.routeVideo.viewKind === "coarse-material-policy-review"
+          status={route
-              ? "M4.8 сохраняет truth-backed сравнение моделей, а штатный M4.7 синхронно показывает coarse material evidence, frozen YOLOX vetoes и causal TGS на всей записи RAVNOVES00. Все слои запечатаны локально и открываются без Worker 006."
+            ? "MULTILAYER RECORDED REVIEW · commands OFF · route truth отсутствует"
-              : "M4.8 сохраняет truth-backed сравнение моделей, а штатный M4.7 viewer показывает фактический DDRNet prediction на всей записи RAVNOVES00. Все 4489 масок запечатаны локально и открываются без Worker 006."
+            : "ROUTE EVIDENCE MISSING · commands OFF"}
-            : "Штатный M4.8-инструмент сравнивает две готовые fine-64 модели на полном GOOSE validation split и на 12 truth-backed hard cases, выбранных только по наличию нужной растительности. Sealed evidence открывается локально без Worker 006."}
-          status={result.routeVideo
-            ? result.routeVideo.viewKind === "coarse-material-policy-review"
-              ? "MULTILAYER POLICY REVIEW · commands OFF · route truth отсутствует"
-              : "DDRNet full-video prediction ready · route truth отсутствует"
-            : "Truth-backed model comparison · route transfer не принят"}
           statusTone="warning"
           facts={[
-            { label: "Источник", value: "GOOSE validation · 962 размеченных кадра · 12 vegetation hard cases" },
+            { label: "Источник", value: "RAVNOVES00 · sensor.camera.right · 4489 recorded frames" },
-            { label: "Сравнение", value: "DDRNet-39 vs PPLiteSeg · official fine-64 weights" },
+            { label: "Город", value: "EoMT Cityscapes · sealed E47 semantic archive" },
-            { label: "Кейсы", value: "трава · куст · ствол · крона · изгородь · лес · посевы" },
+            { label: "Растительность", value: "DDRNet-39 fine-64 → coarse mission-neutral materials" },
-            ...(result.routeVideo ? [{
+            { label: "Safety", value: "YOLOX object boxes + causal TGS · semantic masks не снимают veto" },
-              label: "Видео",
+            { label: "Authority", value: `${rigLabel} · VISUAL REVIEW ONLY · commands OFF` },
-              value: result.routeVideo.viewKind === "coarse-material-policy-review"
-                ? "RAVNOVES00 · 4489/4489 coarse masks + YOLOX + TGS · exact sequence"
-                : "RAVNOVES00 · 4489/4489 DDRNet masks · exact recorded sequence",
-            }] : []),
-            { label: "Authority", value: `${rigLabel} · MODEL QUALIFICATION ONLY · commands OFF` },
           ]}
           brief={{
-            question: "Какие готовые веса лучше различают проезжаемую траву, кусты и стволы на размеченных off-road кадрах?",
+            question: "Можно ли одновременно видеть городской и природный semantic stack, не теряя независимую геометрическую защиту?",
-            approach: "Обе модели последовательно прогнаны в одном изолированном CUDA-runtime на 962 кадрах. 12 визуальных кейсов выбраны детерминированно по truth-поддержке восьми растительных классов; один M4.8 viewer показывает source, truth, prediction и material-error для выбранной модели.",
+            approach: "EoMT и DDRNet сохранены как два независимых sealed слоя на одной M4 timeline. В штатном M4.7 viewer пользователь переключает только отображаемую маску; YOLOX и TGS остаются активными слоями evidence.",
-            principalResult: `${selected.loadedModelName} лидирует по vegetation IoU: ${decimal(selected.vegetationMeanIouPercent, 2)}% против ${decimal(alternative.vegetationMeanIouPercent, 2)}%. ${result.routeVideo?.viewKind === "coarse-material-policy-review" ? "Fine-64 prediction сведён к mission-neutral материалам; YOLOX и TGS сохраняют независимое veto." : result.routeVideo ? "Его фактическая temporal stability теперь видна на всех 4489 кадрах штатного recorded viewer." : "Ошибки по каждому типу проверяются в одном штатном инструменте."}`,
+            principalResult: route
-            limitation: "GOOSE — внешний размеченный домен; RAVNOVES00 — наш fisheye, но без ручной truth-разметки. Материалы — prediction, а не доказательство проходимости. TGS не проецируется в пиксели без отдельной принятой калибровки.",
+              ? "Оба semantic archive доступны в одном viewer. Это не пиксельный fusion и не единая новая модель: городской и природный ответы остаются раздельными."
+              : "Route archive для этой immutable identity отсутствует.",
+            limitation: "RAVNOVES00 не имеет ручной truth. DDRNet заметно прыгает между HIGH GRASS, WOODY и UNKNOWN; поэтому subtype нельзя подавать напрямую в planner. Отсутствие класса никогда не означает свободный путь.",
           }}
           method={{
-            completeness: "complete",
+            completeness: route ? "complete" : "legacy-partial",
             executionClass: "ai-inference",
-            pipelineId: "goose-fine64-ready-weights-to-ravnoves-policy-shadow/v1",
+            pipelineId: "ravnoves-eomt-ddrnet-yolox-causal-tgs-recorded-review/v1",
-            components: result.candidates.map((candidate) => ({
+            components: [
-              kind: "model" as const,
+              {
-              name: candidate.loadedModelName,
+                kind: "model",
-              version: candidate.candidate,
+                name: "EoMT Cityscapes semantic",
-              role: candidate.candidate === result.selectedCandidate ? "selected policy provider" : "comparison candidate",
+                version: "sealed E47 archive",
-              identitySha256: candidate.checkpointSha256,
+                role: "urban semantic review",
-            })),
+                identitySha256: null,
+              },
+              {
+                kind: "model",
+                name: selected.loadedModelName,
+                version: selected.candidate,
+                role: "vegetation material candidate",
+                identitySha256: selected.checkpointSha256,
+              },
+              {
+                kind: "algorithm",
+                name: "Frozen YOLOX + causal TGS",
+                version: "linked M4/M4.9 archives",
+                role: "independent object and geometry veto",
+                identitySha256: null,
+              },
+            ],
           }}
         />
       )}
-      evidence={(
+      evidence={route ? (
-        <>
+        <LaboratoryEvidence
-          <LaboratoryEvidence
+          eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
-            eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
+          title="EoMT CITY / DDRNet VEGETATION + YOLOX + CAUSAL TGS · 4489/4489 · TRUTH отсутствует"
-            title="ERROR: красный — пропуск · жёлтый — лишнее · фиолетовый — перепутан тип · зелёный — совпадение"
+          kind="diagnostic-model"
-            kind="diagnostic-model"
+          resizable
-            resizable
+        >
-          >
+          <VegetationRouteEvidence result={result} />
-            <M48MaskComparisonVisual
+        </LaboratoryEvidence>
-              cases={comparisonCases(result)}
+      ) : (
-              initialCandidate={result.selectedCandidate}
+        <LaboratoryEvidence
-            />
+          eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
-          </LaboratoryEvidence>
+          title="ROUTE ARCHIVE отсутствует"
-          {result.routeVideo ? (
+          kind="diagnostic-model"
-            <LaboratoryEvidence
+        >
-              eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
+          <div className="m4-replay-threat-visual__pane-status" role="alert">
-              title={result.routeVideo.viewKind === "coarse-material-policy-review"
+            Для этой immutable identity нет полного route video evidence.
-                ? "COARSE MATERIAL + YOLOX VETO + CAUSAL TGS · 4489/4489 · TRUTH отсутствует"
+          </div>
-                : "DDRNet PREDICTION · 4489/4489 кадров · TRUTH для этой записи отсутствует"}
+        </LaboratoryEvidence>
-              kind="diagnostic-model"
-              resizable
-            >
-              <VegetationRouteEvidence result={result} />
-            </LaboratoryEvidence>
-          ) : null}
-        </>
       )}
       result={(
         <LaboratoryResultSummary
-          title={result.routeVideo?.viewKind === "coarse-material-policy-review"
+          title="Многослойный visual review собран; управление не авторизовано"
-            ? "Слои собраны для визуального policy review; управление не авторизовано"
+          status="Semantics advisory · YOLOX/TGS veto cannot be cleared"
-            : "DDRNet — стартовые веса; перенос на ровер ещё не доказан"}
-          status={result.routeVideo?.viewKind === "coarse-material-policy-review"
-            ? "Materials are advisory · YOLOX/TGS veto cannot be cleared"
-            : `${selected.loadedModelName} выбран только как vegetation candidate`}
           statusTone="warning"
           metrics={[
             {
-              label: "GOOSE mIoU",
+              label: "Route masks",
-              value: `${decimal(selected.meanIouPercent, 2)}% / ${decimal(alternative.meanIouPercent, 2)}%`,
+              value: route ? `${route.frameCount}/${route.frameCount}` : "0/4489",
-              hint: `${selected.candidate} / ${alternative.candidate} · полный validation split`,
+              hint: "sealed local playback · Worker для открытия не нужен",
             },
             {
-              label: "Vegetation IoU",
+              label: "Semantic sources",
-              value: `${decimal(selected.vegetationMeanIouPercent, 2)}% / ${decimal(alternative.vegetationMeanIouPercent, 2)}%`,
+              value: route ? "2 independent layers" : "0",
-              hint: "агрегация классов grass/vegetation/bush/tree и родственных fine-64 labels",
+              hint: "EoMT CITY / DDRNet VEGETATION · display switches, evidence does not fuse",
             },
             {
-              label: "Worker shadow p95",
+              label: "Vegetation worker p95",
-              value: `${decimal(selected.shadowLatencyP95Ms, 2)} / ${decimal(alternative.shadowLatencyP95Ms, 2)} ms`,
+              value: `${decimal(selected.shadowLatencyP95Ms, 2)} ms`,
-              hint: "чистый inference · одна тяжёлая модель за раз",
+              hint: "изолированный DDRNet inference; не совместный realtime stack",
             },
             {
-              label: "Cold prewarm",
+              label: "Vegetation peak VRAM",
-              value: `${decimal(selected.shadowPrewarmLatencyMs, 1)} / ${decimal(alternative.shadowPrewarmLatencyMs, 1)} ms`,
+              value: `${decimal(selected.peakReservedVramBytes / 1024 ** 3, 2)} GiB`,
-              hint: "один явный inference до допуска кадров; исключён из steady-state p95",
+              hint: "DDRNet candidate на Worker 006",
             },
-            {
-              label: "Worker throughput",
-              value: `${decimal(selected.shadowThroughputFps, 1)} / ${decimal(alternative.shadowThroughputFps, 1)} FPS`,
-              hint: "изолированный Worker 006 · не realtime graph целиком",
-            },
-            {
-              label: "Peak VRAM",
-              value: `${decimal(selected.peakReservedVramBytes / 1024 ** 3, 2)} / ${decimal(alternative.peakReservedVramBytes / 1024 ** 3, 2)} GiB`,
-              hint: `${selected.candidate} / ${alternative.candidate} · RTX 4090`,
-            },
-            {
-              label: "Hard-case evidence",
-              value: "12 truth-backed cases",
-              hint: "8 vegetation strata · Worker для открытия не требуется",
-            },
-            ...(result.routeVideo ? [{
-              label: "Route video",
-              value: "4489/4489 masks",
-              hint: result.routeVideo.viewKind === "coarse-material-policy-review"
-                ? "9 coarse states · YOLOX + causal TGS · Worker-independent playback"
-                : "DDRNet prediction · exact sequence · Worker-independent playback",
-            }] : []),
           ]}
           conclusion={{
-            proved: "Обе официальные fine-64 модели воспроизводимо запускаются на Worker 006; DDRNet лучше по aggregate vegetation IoU. Truth-backed hard cases прямо показывают траву, кусты и стволы, а не случайные автомобили и здания.",
+            proved: "На одной recorded timeline доступны городской EoMT, природный DDRNet, YOLOX detections и causal TGS; LAB автономна от Worker.",
-            notProved: "Не доказаны accuracy на нашем fisheye-домене, папоротник как отдельный материал, collision safety и physical-live поведение ровера. Видео позволяет увидеть temporal stability, но без truth не превращает её в метрику качества.",
+            notProved: "Не доказаны совместный live-runtime EoMT+DDRNet, truth accuracy на fisheye, стабильные vegetation subtypes и безопасное управление ровером.",
-            decision: result.routeVideo?.viewKind === "coarse-material-policy-review"
+            decision: "Использовать маски только для диагностики. Следующий qualification gate — motion-aware temporal vegetation fusion и отдельный совместный realtime load test; до него planner/actuation остаются OFF.",
-              ? "На одном M4.7 проверить ложные LOW GRASS/HIGH GRASS кандидаты против YOLOX и TGS. До truth-кейсов и integrated load этот слой не подключать к planner/actuation."
-              : "Смотреть полный prediction на видео и собирать конкретные temporal/domain failure cases. DDRNet остаётся diagnostic candidate; LiDAR/TGS fail-closed геометрию не ослаблять.",
           }}
         />
       )}

apps/control-station/src/workspaces/laboratory/laboratoryArchiveProfiles.ts

@@ -10,6 +10,7 @@ export type LaboratoryProfileId =
   | "rig-camera-local-surface-v1"
   | "rig-track-geometry-temporal-v1"
   | "rig-ravnoves-perception-gate-v1"
+  | "rig-goose-vegetation-benchmark-v1"
   | "rig-pointpillars-transfer-v1"
   | "rig-right-yolox-lidar-range-v1"
   | "rig-nvidia-ready-stack-v1"
@@ -63,12 +64,19 @@ interface KnownWorkDefinition {
 const rig = (rigLabel: string): string => rigLabel.trim() || "Сенсорный риг";
 
 const KNOWN_WORKS: Readonly<Record<Exclude<LaboratoryWorkId, `session:${string}`>, KnownWorkDefinition>> = {
+  "lab-v1-vegetation-benchmark": {
+    profileId: "rig-goose-vegetation-benchmark-v1",
+    profileName: (rigLabel) => `${rig(rigLabel)} · GOOSE vegetation archive`,
+    experimentId: "lab-v1-vegetation-benchmark-archive",
+    experimentName: "DDRNet vs PPLiteSeg · truth-backed archival comparison",
+    variantName: "M4.8 · GOOSE truth · архивный анализ моделей",
+  },
   "lab-v1-vegetation-shadow": {
     profileId: "rig-ravnoves-perception-gate-v1",
-    profileName: (rigLabel) => `${rig(rigLabel)} · GOOSE vegetation qualification`,
+    profileName: (rigLabel) => `${rig(rigLabel)} · RAVNOVES00 rover perception gate`,
     experimentId: "lab-v1-vegetation-mission-policy",
-    experimentName: "DDRNet vs PPLiteSeg · truth-backed vegetation hard cases",
+    experimentName: "RAVNOVES00 · city + vegetation + TGS review",
-    variantName: "LAB V1 · готовые vegetation weights · GOOSE truth",
+    variantName: "LAB V1 · EoMT + DDRNet + YOLOX + TGS · commands OFF",
   },
   "m48-object-centric-quality": {
     profileId: "rig-dual-evidence-virtual-corridor-v1",

apps/control-station/src/workspaces/laboratory/useAdvancedLaboratoryCatalog.ts

@@ -18,6 +18,7 @@ function mergeResults(
   next: AdvancedLaboratoryResults,
 ): AdvancedLaboratoryResults {
   return {
+    vegetationBenchmark: next.vegetationBenchmark ?? current.vegetationBenchmark,
     vegetationShadow: next.vegetationShadow ?? current.vegetationShadow,
     m47Graph: next.m47Graph ?? current.m47Graph,
     m48: next.m48 ?? current.m48,
@@ -122,6 +123,7 @@ export function useAdvancedLaboratoryCatalog({
     const indexedResultId = index.find((item) => item.workId === selectedWorkId)?.resultId;
     if (
       [
+        "lab-v1-vegetation-benchmark",
         "lab-v1-vegetation-shadow",
         "m47-reference-graph-shadow",
         "m48-object-centric-quality",

apps/control-station/test/m49TgsFullShadow.test.mjs

@@ -108,8 +108,13 @@ test("M4.9T5 viewer prefers autonomous chunks and keeps a sealed legacy fallback
   assert.doesNotMatch(source, /centersXyM\.map\(/);
   assert.match(source, /fetchE47SemanticSlamResult/);
   assert.match(source, /next\.baseM4ResultId !== result\.source\.linkedVisualResultId/);
-  assert.match(source, /semantic=\{semanticOverride \?\? \(semantic \? \{/);
+  assert.match(source, /semanticLayers=\{semanticLayers\}/);
+  assert.match(source, /ГОРОД · EoMT/);
+  assert.match(source, /ПРИРОДА · DDRNet/);
   assert.match(source, /semanticOverride/);
+  assert.doesNotMatch(source, /if \(semanticOverride\) return/);
+  assert.match(visual, /label="Источник семантики"/);
+  assert.match(visual, /availableSemanticLayers\.length > 1/);
   assert.doesNotMatch(visual, /classifiedSpatialLayer \|\| !showReferenceMediaLayers \? \[\]/);
   assert.match(
     visual,

apps/control-station/test/vegetationShadow.test.mjs

@@ -5,6 +5,7 @@ import { after, before, test } from "node:test";
 import { createServer } from "vite";
 
 let server;
+let fetchVegetationBenchmarkResult;
 let fetchVegetationShadowResult;
 
 before(async () => {
@@ -13,7 +14,7 @@ before(async () => {
     logLevel: "silent",
     server: { middlewareMode: true },
   });
-  ({ fetchVegetationShadowResult } = await server.ssrLoadModule(
+  ({ fetchVegetationBenchmarkResult, fetchVegetationShadowResult } = await server.ssrLoadModule(
     "/src/core/laboratory/vegetationShadow.ts",
   ));
 });
@@ -23,6 +24,7 @@ after(async () => {
 });
 
 const resultId = `lab-v1-vegetation-shadow-${"a".repeat(64)}`;
+const benchmarkResultId = `lab-v1-vegetation-benchmark-${"d".repeat(64)}`;
 
 function candidate(candidateKey, vegetationIou) {
   return {
@@ -111,21 +113,26 @@ function coarseRouteVideo() {
     linked_tgs_result_id: `m49-tgs-full-shadow-${"2".repeat(64)}`,
     taxonomy: {
       schema_version: "missioncore.lab-v1-terrain-policy-taxonomy/v1",
-      classes: Array.from({ length: 9 }, (_, classId) => ({
+      classes: Array.from({ length: 10 }, (_, classId) => ({
         class_id: classId,
         label: `policy-${classId}`,
         color_rgb: [classId, classId, classId],
-        disposition: classId === 0 ? "ambiguous" : "prediction",
+        disposition: classId === 0 ? "ambiguous" : classId === 9 ? "undefined" : "prediction",
-        material_class: classId === 0 ? null : "grass",
+        material_class: classId === 0 || classId === 9 ? null : "grass",
-        evidence_state: classId === 0 ? "UNOBSERVED" : "SUPPORTED_GROUND",
+        evidence_state: classId === 0 || classId === 9 ? "UNOBSERVED" : "SUPPORTED_GROUND",
       })),
     },
-    aggregate_prediction_pixels: Array(9).fill(0),
+    aggregate_prediction_pixels: Array(10).fill(0),
     mask_archive: {
       path: "video/coarse-material-policy-masks.zip",
       sha256: "8".repeat(64),
       byte_length: 2048,
     },
+    valid_fov: {
+      mask_path: "video/valid-fov-mask.png",
+      mask_sha256: "7".repeat(64),
+      outside_valid_fov_class_id: 9,
+    },
     policy: {
       presets: {
         urban: { grass: "NO_GO" },
@@ -220,23 +227,55 @@ test("vegetation LAB parses coarse material policy and sealed TGS binding", asyn
   });
   assert.equal(result.routeVideo.viewKind, "coarse-material-policy-review");
   assert.match(result.routeVideo.linkedTgsResultId, /^m49-tgs-full-shadow-/);
-  assert.equal(result.routeVideo.taxonomy.length, 9);
+  assert.equal(result.routeVideo.taxonomy.length, 10);
   assert.equal(result.routeVideo.taxonomy[0].evidenceState, "UNOBSERVED");
   assert.equal(result.routeVideo.policyPresets.urban.grass, "NO_GO");
   assert.equal(result.routeVideo.fusionMode, "synchronised-multilayer-review");
 });
 
-test("vegetation LAB reuses the admitted M4.8 and M4.7 instruments", async () => {
+test("vegetation GOOSE benchmark opens through its separate archival endpoint", async () => {
-  const resultSource = await readFile(
+  let requestedUrl = "";
-    new URL("../src/workspaces/laboratory/VegetationShadowResult.tsx", import.meta.url),
+  const result = await fetchVegetationBenchmarkResult(benchmarkResultId, {
-    "utf8",
+    fetcher: async (url) => {
+      requestedUrl = String(url);
+      return new Response(JSON.stringify({
+        ...labPayload(null),
+        result_id: benchmarkResultId,
+      }), {
+        status: 200,
+        headers: { "Content-Type": "application/json" },
+      });
+    },
+  });
+  assert.equal(
+    requestedUrl,
+    `/api/v1/laboratory/vegetation-benchmark/${benchmarkResultId}`,
   );
-  assert.match(resultSource, /M48MaskComparisonVisual/);
+  assert.equal(result.routeVideo, null);
+  assert.equal(result.validationCases.length, 12);
+});
+
+test("vegetation realtime LAB and archival benchmark use separate admitted instruments", async () => {
+  const [resultSource, benchmarkSource] = await Promise.all([
+    readFile(
+      new URL("../src/workspaces/laboratory/VegetationShadowResult.tsx", import.meta.url),
+      "utf8",
+    ),
+    readFile(
+      new URL("../src/workspaces/laboratory/VegetationBenchmarkResult.tsx", import.meta.url),
+      "utf8",
+    ),
+  ]);
+  assert.doesNotMatch(resultSource, /M48MaskComparisonVisual/);
   assert.match(resultSource, /M4ReplayThreatVisual/);
   assert.match(resultSource, /M49TgsFullShadowEvidence/);
   assert.match(resultSource, /semanticOverride/);
+  assert.match(resultSource, /EoMT CITY \/ DDRNet VEGETATION/);
   assert.equal(resultSource.match(/<LaboratoryEvidence\b/g)?.length, 2);
   assert.match(resultSource, /linkedTgsResultId/);
+  assert.match(benchmarkSource, /M48MaskComparisonVisual/);
+  assert.doesNotMatch(benchmarkSource, /M49TgsFullShadowEvidence/);
+  assert.equal(benchmarkSource.match(/<LaboratoryEvidence\b/g)?.length, 1);
   assert.doesNotMatch(resultSource, /VegetationRouteVisual|urban\/rural\/off-road presets/);
   await assert.rejects(
     access(new URL("../src/workspaces/laboratory/VegetationShadowVisual.tsx", import.meta.url)),

config/laboratories/lab-v1-vegetation-benchmark.json

@@ -0,0 +1,10 @@
+{
+  "schema_version": "missioncore.laboratory-evidence-definition/v1",
+  "work_id": "lab-v1-vegetation-benchmark",
+  "evidence": {
+    "runtime_relative_root": "lab-v1-vegetation-benchmark/results",
+    "result_id_prefix": "lab-v1-vegetation-benchmark",
+    "document_name": "result.json",
+    "schema_version": "missioncore.lab-v1-vegetation-shadow/v1"
+  }
+}

config/laboratory-execution.json

@@ -212,6 +212,7 @@
     }
   ],
   "legacy_work_ids": [
+    "lab-v1-vegetation-benchmark",
     "m48r3-static-occupancy-shadow",
     "m47-reference-graph-shadow",
     "e31-source-binding",

config/laboratory-value-review.json

@@ -282,10 +282,17 @@
       "lifecycle": "current",
       "visual_evidence": "available"
     },
+    {
+      "catalog_id": "lab-v1-vegetation-benchmark",
+      "evidence_id": "lab-v1-vegetation-benchmark-a8944d6c2d1102d81da78bcb4963760c9288db0421d9f2686afcbdd14b610d3d",
+      "signal": "progress",
+      "lifecycle": "current",
+      "visual_evidence": "available"
+    },
     {
       "catalog_id": "lab-v1-vegetation-shadow",
-      "evidence_id": "lab-v1-vegetation-shadow-ad4d9fbbb21ff8a270b77f559b4e78dcdaf0455afd61afb5033009623984e554",
+      "evidence_id": "lab-v1-vegetation-shadow-394a5bca860e49a619a90c0f267259fd17e550a0abc4b51f821b1f482040c00e",
-      "signal": "failed",
+      "signal": "progress",
       "lifecycle": "current",
       "visual_evidence": "available"
     }

src/k1link/laboratory/vegetation_benchmark_archive.py

@@ -0,0 +1,139 @@
+"""Seal a benchmark-only vegetation result into its archival LAB namespace."""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import hashlib
+import json
+import shutil
+import tempfile
+from pathlib import Path, PurePosixPath
+from typing import Any, Final
+
+from k1link.laboratory.evidence_registry import LaboratoryEvidenceDefinition
+from k1link.laboratory.evidence_report import verify_laboratory_evidence_result
+from k1link.laboratory.vegetation_shadow_lab import LAB_SCHEMA
+
+_SOURCE_DEFINITION: Final = LaboratoryEvidenceDefinition(
+    work_id="lab-v1-vegetation-shadow",
+    runtime_relative_root=PurePosixPath("lab-v1-vegetation/results"),
+    result_id_prefix="lab-v1-vegetation-shadow",
+    document_name="result.json",
+    result_schema_version=LAB_SCHEMA,
+)
+_ARCHIVE_DEFINITION: Final = LaboratoryEvidenceDefinition(
+    work_id="lab-v1-vegetation-benchmark",
+    runtime_relative_root=PurePosixPath("lab-v1-vegetation-benchmark/results"),
+    result_id_prefix="lab-v1-vegetation-benchmark",
+    document_name="result.json",
+    result_schema_version=LAB_SCHEMA,
+)
+
+
+class VegetationBenchmarkArchiveError(ValueError):
+    """The source result is not a valid benchmark-only immutable result."""
+
+
+def _canonical_json(value: object) -> bytes:
+    return json.dumps(
+        value,
+        ensure_ascii=False,
+        separators=(",", ":"),
+        sort_keys=True,
+    ).encode("utf-8")
+
+
+def _object(value: object, label: str) -> dict[str, Any]:
+    if not isinstance(value, dict):
+        raise VegetationBenchmarkArchiveError(f"{label} is invalid")
+    return value
+
+
+def seal_vegetation_benchmark_archive(
+    *,
+    source_result_root: Path,
+    output_root: Path,
+) -> Path:
+    source = source_result_root.resolve(strict=True)
+    verify_laboratory_evidence_result(_SOURCE_DEFINITION, source)
+    manifest = _object(
+        json.loads((source / "result.json").read_text("utf-8")),
+        "source result",
+    )
+    if manifest.get("route_video") is not None:
+        raise VegetationBenchmarkArchiveError("benchmark archive source contains route video")
+    artifacts = manifest.get("artifacts")
+    if not isinstance(artifacts, list):
+        raise VegetationBenchmarkArchiveError("source artifacts are invalid")
+
+    identity = copy.deepcopy(_object(manifest.get("identity"), "source identity"))
+    identity.update(
+        {
+            "lab_id": "lab-v1-vegetation-benchmark-archive",
+            "archived_from_result_id": source.name,
+        }
+    )
+    identity_sha256 = hashlib.sha256(_canonical_json(identity)).hexdigest()
+    result_id = f"lab-v1-vegetation-benchmark-{identity_sha256}"
+
+    output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
+    destination = output_root / result_id
+    if destination.exists():
+        verify_laboratory_evidence_result(_ARCHIVE_DEFINITION, destination)
+        return destination
+
+    temporary = Path(tempfile.mkdtemp(prefix=".vegetation-benchmark-", dir=output_root))
+    try:
+        for raw in artifacts:
+            descriptor = _object(raw, "artifact descriptor")
+            relative_text = descriptor.get("path")
+            if not isinstance(relative_text, str):
+                raise VegetationBenchmarkArchiveError("artifact path is invalid")
+            relative = PurePosixPath(relative_text)
+            if relative.is_absolute() or any(part in {"", ".", ".."} for part in relative.parts):
+                raise VegetationBenchmarkArchiveError("artifact path is unsafe")
+            source_path = source.joinpath(*relative.parts)
+            destination_path = temporary.joinpath(*relative.parts)
+            destination_path.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
+            shutil.copyfile(source_path, destination_path)
+
+        archived = copy.deepcopy(manifest)
+        archived.update(
+            {
+                "result_id": result_id,
+                "identity": identity,
+                "identity_sha256": identity_sha256,
+                "archived_from_result_id": source.name,
+            }
+        )
+        (temporary / "result.json").write_bytes(_canonical_json(archived) + b"\n")
+        temporary.rename(destination)
+        verify_laboratory_evidence_result(_ARCHIVE_DEFINITION, destination)
+        return destination
+    except Exception:
+        shutil.rmtree(temporary, ignore_errors=True)
+        raise
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source-result-root", type=Path, required=True)
+    parser.add_argument("--output-root", type=Path, required=True)
+    args = parser.parse_args()
+    print(
+        seal_vegetation_benchmark_archive(
+            source_result_root=args.source_result_root,
+            output_root=args.output_root,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()
+
+
+__all__ = [
+    "VegetationBenchmarkArchiveError",
+    "seal_vegetation_benchmark_archive",
+]

src/k1link/laboratory/vegetation_policy_review.py

@@ -112,6 +112,7 @@ def seal_vegetation_policy_review(
     mission_policy_path: Path,
     provider_label_map_path: Path,
     m49_tgs_full_shadow_root: Path,
+    valid_fov_mask_path: Path,
     output_root: Path,
     created_at_utc: str | None = None,
 ) -> Path:
@@ -142,6 +143,7 @@ def seal_vegetation_policy_review(
         raise VegetationPolicyReviewError("fine mask archive identity changed")
     raw_archive_path = base_root / "video" / "ddrnet-semantic-masks.zip"
     fine_taxonomy = _object(base_route.get("taxonomy"), "fine taxonomy")
+    valid_fov_source = valid_fov_mask_path.resolve(strict=True)
 
     output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
     temporary = Path(tempfile.mkdtemp(prefix=".lab-v1-policy-", dir=output_root))
@@ -152,11 +154,23 @@ def seal_vegetation_policy_review(
             artifacts=base.get("artifacts"),
         )
         policy_archive = temporary / "video" / "coarse-material-policy-masks.zip"
+        valid_fov_destination = temporary / "video" / "valid-fov-mask.png"
+        valid_fov_destination.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
+        shutil.copyfile(valid_fov_source, valid_fov_destination)
+        valid_fov_proof = {
+            "role": "route-camera-valid-fov-mask",
+            "path": "video/valid-fov-mask.png",
+            "byte_length": valid_fov_destination.stat().st_size,
+            "sha256": sha256_path(valid_fov_destination),
+            "media_type": "image/png",
+        }
+        artifacts.append(valid_fov_proof)
         policy_counts = build_policy_mask_archive(
             source_archive=raw_archive_path,
             destination_archive=policy_archive,
             fine_taxonomy=fine_taxonomy,
             provider_label_map=provider_map,
+            valid_fov_mask=valid_fov_destination,
         )
         policy_archive_proof = {
             "role": "route-coarse-material-mask-archive",
@@ -180,6 +194,11 @@ def seal_vegetation_policy_review(
                 "taxonomy": policy_taxonomy(),
                 "aggregate_prediction_pixels": policy_counts,
                 "linked_tgs_result_id": tgs.result_id,
+                "valid_fov": {
+                    "mask_path": valid_fov_proof["path"],
+                    "mask_sha256": valid_fov_proof["sha256"],
+                    "outside_valid_fov_class_id": 9,
+                },
                 "policy": {
                     "profile_id": mission_policy["profile_id"],
                     "profile_sha256": sha256_path(mission_policy_path),
@@ -196,6 +215,7 @@ def seal_vegetation_policy_review(
                     "spatial_safety_veto_layer": "M4.9 full TGS gravity-local costmap",
                     "temporal_consensus_owner": "TGS causal rolling 1 s and metric obstacle tracks",
                     "camera_semantic_temporal_filter": "none",
+                    "camera_valid_fov_filter": "sealed exact KB4 valid-FOV mask",
                     "reason": "No admitted TGS-to-camera pixel projection exists.",
                 },
             }
@@ -238,7 +258,7 @@ def seal_vegetation_policy_review(
                         "Vegetation semantics never clears YOLOX, LiDAR, metric obstacle "
                         "or TGS vetoes."
                     ),
-                    "Undefined pixels outside the 600x600 center crop remain fail-closed.",
+                    "Pixels outside the exact KB4 valid FOV are transparent UNOBSERVED evidence.",
                     (
                         "TGS remains in gravity-local space; no uncalibrated pixel "
                         "projection is fabricated."
@@ -269,6 +289,7 @@ def main() -> None:
     parser.add_argument("--mission-policy-path", type=Path, required=True)
     parser.add_argument("--provider-label-map-path", type=Path, required=True)
     parser.add_argument("--m49-tgs-full-shadow-root", type=Path, required=True)
+    parser.add_argument("--valid-fov-mask-path", type=Path, required=True)
     parser.add_argument("--output-root", type=Path, required=True)
     args = parser.parse_args()
     print(seal_vegetation_policy_review(**vars(args)))

src/k1link/laboratory/vegetation_policy_video.py

@@ -90,6 +90,14 @@ POLICY_CLASSES: Final = (
         "material_class": "vegetation_unknown",
         "evidence_state": "VEGETATION_UNKNOWN",
     },
+    {
+        "class_id": 9,
+        "label": "OUTSIDE VALID FOV · NO SENSOR EVIDENCE",
+        "color_rgb": [0, 0, 0],
+        "disposition": "undefined",
+        "material_class": None,
+        "evidence_state": "UNOBSERVED",
+    },
 )
 
 _MATERIAL_TO_CLASS: Final = {
@@ -155,10 +163,18 @@ def build_policy_mask_archive(
     destination_archive: Path,
     fine_taxonomy: dict[str, object],
     provider_label_map: dict[str, Any],
+    valid_fov_mask: Path,
 ) -> list[int]:
     """Map every fine mask to coarse evidence; safety vetoes remain separate layers."""
 
     lut = fine_to_policy_lut(fine_taxonomy, provider_label_map)
+    try:
+        with Image.open(valid_fov_mask) as image:
+            valid_fov = np.asarray(image.convert("L"), dtype=np.uint8) > 0
+    except OSError as exc:
+        raise VegetationPolicyVideoError("valid-FOV mask is unreadable") from exc
+    if valid_fov.shape != (HEIGHT, WIDTH) or not np.any(valid_fov) or np.all(valid_fov):
+        raise VegetationPolicyVideoError("valid-FOV mask geometry is invalid")
     counts = np.zeros(len(POLICY_CLASSES), dtype=np.int64)
     destination_archive.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
     try:
@@ -175,6 +191,7 @@ def build_policy_mask_archive(
                         f"fine mask {member} has shape {fine.shape}, expected {(HEIGHT, WIDTH)}"
                     )
                 coarse = lut[fine]
+                coarse[~valid_fov] = 9
                 counts += np.bincount(
                     coarse.reshape(-1),
                     minlength=len(POLICY_CLASSES),

src/k1link/laboratory/vegetation_shadow_lab.py

@@ -327,6 +327,7 @@ def seal_vegetation_shadow_lab(
     mission_policy_path: Path | None = None,
     provider_label_map_path: Path | None = None,
     m49_tgs_full_shadow_root: Path | None = None,
+    valid_fov_mask_path: Path | None = None,
 ) -> Path:
     roots = {
         ("ddrnet", "goose"): ddrnet_goose_root.resolve(),
@@ -349,6 +350,7 @@ def seal_vegetation_shadow_lab(
         mission_policy_path,
         provider_label_map_path,
         m49_tgs_full_shadow_root,
+        valid_fov_mask_path,
     )
     if any(value is not None for value in policy_inputs) and not all(
         value is not None for value in policy_inputs
@@ -385,6 +387,7 @@ def seal_vegetation_shadow_lab(
         mission_policy_path is not None
         and provider_label_map_path is not None
         and m49_tgs_full_shadow_root is not None
+        and valid_fov_mask_path is not None
         and route_video is not None
     ):
         repository_root = mission_policy_path.resolve().parents[2]
@@ -543,13 +546,25 @@ def seal_vegetation_shadow_lab(
                 and linked_tgs_result_id is not None
                 and mission_policy_path is not None
                 and provider_label_map_path is not None
+                and valid_fov_mask_path is not None
             ):
                 policy_archive = temporary / "video" / "coarse-material-policy-masks.zip"
+                valid_fov_destination = temporary / "video" / "valid-fov-mask.png"
+                shutil.copyfile(valid_fov_mask_path.resolve(strict=True), valid_fov_destination)
+                valid_fov_descriptor = {
+                    "role": "route-camera-valid-fov-mask",
+                    "path": "video/valid-fov-mask.png",
+                    "byte_length": valid_fov_destination.stat().st_size,
+                    "sha256": sha256_path(valid_fov_destination),
+                    "media_type": "image/png",
+                }
+                artifacts.append(valid_fov_descriptor)
                 policy_counts = build_policy_mask_archive(
                     source_archive=route_video_archive,
                     destination_archive=policy_archive,
                     fine_taxonomy=_object(route_video["taxonomy"], "fine video taxonomy"),
                     provider_label_map=provider_label_map,
+                    valid_fov_mask=valid_fov_destination,
                 )
                 policy_descriptor = {
                     "role": "route-coarse-material-mask-archive",
@@ -571,6 +586,11 @@ def seal_vegetation_shadow_lab(
                         "taxonomy": policy_taxonomy(),
                         "aggregate_prediction_pixels": policy_counts,
                         "linked_tgs_result_id": linked_tgs_result_id,
+                        "valid_fov": {
+                            "mask_path": valid_fov_descriptor["path"],
+                            "mask_sha256": valid_fov_descriptor["sha256"],
+                            "outside_valid_fov_class_id": 9,
+                        },
                         "policy": {
                             "profile_id": mission_policy["profile_id"],
                             "profile_sha256": sha256_path(mission_policy_path),
@@ -591,6 +611,7 @@ def seal_vegetation_shadow_lab(
                                 "TGS causal rolling 1 s and metric obstacle tracks"
                             ),
                             "camera_semantic_temporal_filter": "none",
+                            "camera_valid_fov_filter": "sealed exact KB4 valid-FOV mask",
                             "reason": "No admitted TGS-to-camera pixel projection exists.",
                         },
                     }
@@ -680,7 +701,11 @@ def seal_vegetation_shadow_lab(
                     )
                 ),
                 "Vegetation semantics never clears rigid LiDAR/TGS occupancy.",
-                "Undefined pixels outside the 600x600 center crop remain fail-closed.",
+                (
+                    "Pixels outside the exact KB4 valid FOV are transparent UNOBSERVED evidence."
+                    if mission_policy is not None
+                    else "Undefined pixels outside the 600x600 center crop remain fail-closed."
+                ),
                 *(
                     [
                         (
@@ -723,6 +748,7 @@ def _parse_args() -> argparse.Namespace:
     parser.add_argument("--mission-policy-path", type=Path)
     parser.add_argument("--provider-label-map-path", type=Path)
     parser.add_argument("--m49-tgs-full-shadow-root", type=Path)
+    parser.add_argument("--valid-fov-mask-path", type=Path)
     return parser.parse_args()
 
 
@@ -739,6 +765,7 @@ def main() -> None:
         mission_policy_path=args.mission_policy_path,
         provider_label_map_path=args.provider_label_map_path,
         m49_tgs_full_shadow_root=args.m49_tgs_full_shadow_root,
+        valid_fov_mask_path=args.valid_fov_mask_path,
     )
     print(destination)
 

src/k1link/web/app.py

@@ -138,7 +138,6 @@ from k1link.web.m49_physical_safety_playback_api import (
 )
 from k1link.web.m49_tgs_fail_closed_api import build_m49_tgs_fail_closed_router
 from k1link.web.m49_tgs_full_shadow_api import build_m49_tgs_full_shadow_router
-from k1link.web.vegetation_shadow_lab_api import build_vegetation_shadow_lab_router
 from k1link.web.map_api import (
     MapGatewayConfiguration,
     MapGatewayProxy,
@@ -165,6 +164,10 @@ from k1link.web.session_api import build_session_router
 from k1link.web.simulation_projects_api import build_simulation_projects_router
 from k1link.web.simulation_world_provider_api import build_simulation_world_provider_router
 from k1link.web.system_telemetry_api import build_system_telemetry_router
+from k1link.web.vegetation_shadow_lab_api import (
+    build_vegetation_benchmark_lab_router,
+    build_vegetation_shadow_lab_router,
+)
 from k1link.web.viewer_diagnostics_api import build_viewer_diagnostics_router
 
 REPOSITORY_ROOT = Path(__file__).resolve().parents[3]
@@ -1031,6 +1034,17 @@ app.include_router(
         ),
     )
 )
+app.include_router(
+    build_vegetation_benchmark_lab_router(
+        root_provider=lambda: (
+            REPOSITORY_ROOT
+            / ".runtime"
+            / "compute-experiments"
+            / "lab-v1-vegetation-benchmark"
+            / "results"
+        ),
+    )
+)
 app.include_router(
     build_m49_physical_safety_playback_router(
         root_provider=lambda: (

src/k1link/web/vegetation_shadow_lab_api.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 import copy
 import hashlib
 import json
-import re
 import zipfile
 from collections.abc import Callable
 from functools import lru_cache
@@ -20,10 +19,9 @@ from k1link.laboratory.evidence_report import (
     LaboratoryEvidenceReportError,
     verify_laboratory_evidence_result,
 )
-from k1link.laboratory.vegetation_shadow_lab import LAB_SCHEMA, RESULT_PREFIX
+from k1link.laboratory.vegetation_shadow_lab import LAB_SCHEMA
 
 RootProvider = Callable[[], Path | None]
-RESULT_ID: Final = re.compile(rf"^{re.escape(RESULT_PREFIX)}[a-f0-9]{{64}}$")
 _MAX_DOCUMENT_BYTES: Final = 1024 * 1024
 _DEFINITION: Final = LaboratoryEvidenceDefinition(
     work_id="lab-v1-vegetation-shadow",
@@ -32,25 +30,55 @@ _DEFINITION: Final = LaboratoryEvidenceDefinition(
     document_name="result.json",
     result_schema_version=LAB_SCHEMA,
 )
+_BENCHMARK_DEFINITION: Final = LaboratoryEvidenceDefinition(
+    work_id="lab-v1-vegetation-benchmark",
+    runtime_relative_root=PurePosixPath("lab-v1-vegetation-benchmark/results"),
+    result_id_prefix="lab-v1-vegetation-benchmark",
+    document_name="result.json",
+    result_schema_version=LAB_SCHEMA,
+)
 
 
 def build_vegetation_shadow_lab_router(
     *, root_provider: RootProvider = lambda: None,
 ) -> APIRouter:
-    router = APIRouter(
+    return _build_vegetation_lab_router(
         prefix="/api/v1/laboratory/vegetation-shadow",
+        definition=_DEFINITION,
+        root_provider=root_provider,
+    )
+
+
+def build_vegetation_benchmark_lab_router(
+    *, root_provider: RootProvider = lambda: None,
+) -> APIRouter:
+    return _build_vegetation_lab_router(
+        prefix="/api/v1/laboratory/vegetation-benchmark",
+        definition=_BENCHMARK_DEFINITION,
+        root_provider=root_provider,
+    )
+
+
+def _build_vegetation_lab_router(
+    *,
+    prefix: str,
+    definition: LaboratoryEvidenceDefinition,
+    root_provider: RootProvider,
+) -> APIRouter:
+    router = APIRouter(
+        prefix=prefix,
         tags=["laboratory"],
     )
 
     @router.get("/{result_id}")
     def get_result(result_id: str) -> dict[str, object]:
-        candidate = _resolve_candidate(root_provider, result_id)
+        candidate = _resolve_candidate(root_provider, definition, result_id)
-        return {**copy.deepcopy(_read_verified(candidate)), "access": "read-only"}
+        return {**copy.deepcopy(_read_verified(candidate, definition)), "access": "read-only"}
 
     @router.get("/{result_id}/assets/{asset_path:path}")
     def get_asset(result_id: str, asset_path: str) -> FileResponse:
-        candidate = _resolve_candidate(root_provider, result_id)
+        candidate = _resolve_candidate(root_provider, definition, result_id)
-        manifest = _read_verified(candidate)
+        manifest = _read_verified(candidate, definition)
         artifacts = manifest.get("artifacts")
         if not isinstance(artifacts, list):
             raise HTTPException(status_code=404, detail="Vegetation LAB asset not found")
@@ -90,8 +118,8 @@ def build_vegetation_shadow_lab_router(
 
     @router.get("/{result_id}/masks/{sequence}")
     def get_video_mask(result_id: str, sequence: int) -> Response:
-        candidate = _resolve_candidate(root_provider, result_id)
+        candidate = _resolve_candidate(root_provider, definition, result_id)
-        manifest = _read_verified(candidate)
+        manifest = _read_verified(candidate, definition)
         route_video = manifest.get("route_video")
         if (
             not isinstance(route_video, dict)
@@ -168,9 +196,13 @@ def _configured_root(provider: RootProvider) -> Path | None:
     return root if root.is_dir() else None
 
 
-def _resolve_candidate(provider: RootProvider, result_id: str) -> Path:
+def _resolve_candidate(
+    provider: RootProvider,
+    definition: LaboratoryEvidenceDefinition,
+    result_id: str,
+) -> Path:
     root = _configured_root(provider)
-    if root is None or RESULT_ID.fullmatch(result_id) is None:
+    if root is None or definition.result_id_pattern.fullmatch(result_id) is None:
         raise HTTPException(status_code=404, detail="Vegetation LAB result not found")
     candidate = root / result_id
     if candidate.is_symlink():
@@ -184,7 +216,10 @@ def _resolve_candidate(provider: RootProvider, result_id: str) -> Path:
     return resolved
 
 
-def _read_verified(candidate: Path) -> dict[str, Any]:
+def _read_verified(
+    candidate: Path,
+    definition: LaboratoryEvidenceDefinition,
+) -> dict[str, Any]:
     try:
         rows: list[tuple[str, int, int, int, int]] = []
         for path in sorted(candidate.rglob("*"), key=lambda item: item.as_posix()):
@@ -202,19 +237,39 @@ def _read_verified(candidate: Path) -> dict[str, Any]:
             status_code=503,
             detail="Vegetation LAB evidence failed verification",
         ) from None
-    return _read_verified_cached(str(candidate), signature)
+    return _read_verified_cached(
+        str(candidate),
+        signature,
+        definition.work_id,
+        str(definition.runtime_relative_root),
+        definition.result_id_prefix,
+        definition.document_name,
+        definition.result_schema_version,
+    )
 
 
 @lru_cache(maxsize=16)
 def _read_verified_cached(
     candidate_text: str,
     signature: tuple[tuple[str, int, int, int, int], ...],
+    work_id: str,
+    runtime_relative_root: str,
+    result_id_prefix: str,
+    document_name: str,
+    result_schema_version: str,
 ) -> dict[str, Any]:
     del signature
     candidate = Path(candidate_text)
+    definition = LaboratoryEvidenceDefinition(
+        work_id=work_id,
+        runtime_relative_root=PurePosixPath(runtime_relative_root),
+        result_id_prefix=result_id_prefix,
+        document_name=document_name,
+        result_schema_version=result_schema_version,
+    )
     try:
-        verify_laboratory_evidence_result(_DEFINITION, candidate)
+        verify_laboratory_evidence_result(definition, candidate)
-        path = candidate / "result.json"
+        path = candidate / definition.document_name
         if path.stat().st_size > _MAX_DOCUMENT_BYTES:
             raise LaboratoryEvidenceReportError("Vegetation LAB document is too large")
         payload = json.loads(path.read_text("utf-8"))
@@ -228,4 +283,7 @@ def _read_verified_cached(
     return payload
 
 
-__all__ = ["build_vegetation_shadow_lab_router"]
+__all__ = [
+    "build_vegetation_benchmark_lab_router",
+    "build_vegetation_shadow_lab_router",
+]

tests/test_laboratory_evidence_registry.py

@@ -127,9 +127,10 @@ def test_product_registry_declares_every_advanced_evidence_source() -> None:
         repository_root / "config" / "laboratories"
     )
 
-    assert len(registry.definitions) == 43
+    assert len(registry.definitions) == 44
     assert {item.work_id for item in registry.definitions} >= {
         "lab-v1-vegetation-shadow",
+        "lab-v1-vegetation-benchmark",
         "e31-source-binding",
         "e46j-raw-fisheye-realtime",
         "e47-semantic-slam-shadow",

tests/test_laboratory_value_review_registry.py

@@ -80,7 +80,7 @@ def test_product_value_review_registry_covers_reviewed_laboratory_families() ->
         root / "config" / "laboratory-value-review.json"
     )
 
-    assert len(registry.entries) == 41
+    assert len(registry.entries) == 42
     assert {entry.catalog_id for entry in registry.entries} >= {
         "e28-local-surface",
         "e46d-temporal-failure-audit",
@@ -94,4 +94,5 @@ def test_product_value_review_registry_covers_reviewed_laboratory_families() ->
         "m49-tgs-fail-closed-evidence",
         "m49-tgs-full-shadow",
         "lab-v1-vegetation-shadow",
+        "lab-v1-vegetation-benchmark",
     }

tests/test_vegetation_shadow_lab.py

@@ -1,16 +1,20 @@
 from __future__ import annotations
 
 import hashlib
+import io
 import json
 import shutil
 import zipfile
 from pathlib import Path
 from types import SimpleNamespace
 
+import numpy as np
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
+from PIL import Image
 
 import k1link.laboratory.vegetation_policy_review as policy_review_module
+import k1link.laboratory.vegetation_policy_video as policy_video_module
 import k1link.laboratory.vegetation_shadow_lab as vegetation_lab_module
 from k1link.laboratory import LaboratoryEvidenceRegistry
 from k1link.laboratory.evidence_report import verify_laboratory_evidence_result
@@ -21,6 +25,45 @@ from k1link.web.vegetation_shadow_lab_api import build_vegetation_shadow_lab_rou
 REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
 
 
+def test_coarse_policy_masks_mark_every_outside_fov_pixel_undefined(
+    tmp_path: Path,
+    monkeypatch,
+) -> None:
+    monkeypatch.setattr(policy_video_module, "FRAME_COUNT", 1)
+    monkeypatch.setattr(
+        policy_video_module,
+        "fine_to_policy_lut",
+        lambda _taxonomy, _provider_map: np.full(256, 4, dtype=np.uint8),
+    )
+    source = tmp_path / "fine.zip"
+    fine_buffer = io.BytesIO()
+    Image.new("L", (800, 600), color=1).save(fine_buffer, format="PNG")
+    with zipfile.ZipFile(source, "w") as archive:
+        archive.writestr("masks/frame-000001.png", fine_buffer.getvalue())
+
+    valid_fov = np.zeros((600, 800), dtype=np.uint8)
+    valid_fov[:, :400] = 255
+    valid_fov_path = tmp_path / "valid-fov.png"
+    Image.fromarray(valid_fov, mode="L").save(valid_fov_path)
+    destination = tmp_path / "coarse.zip"
+    counts = policy_video_module.build_policy_mask_archive(
+        source_archive=source,
+        destination_archive=destination,
+        fine_taxonomy={},
+        provider_label_map={},
+        valid_fov_mask=valid_fov_path,
+    )
+    with (
+        zipfile.ZipFile(destination) as archive,
+        Image.open(io.BytesIO(archive.read("masks/frame-000001.png"))) as image,
+    ):
+        coarse = np.asarray(image.convert("L"))
+    assert np.all(coarse[:, :400] == 4)
+    assert np.all(coarse[:, 400:] == 9)
+    assert counts[4] == 600 * 400
+    assert counts[9] == 600 * 400
+
+
 def _sha256(path: Path) -> str:
     return hashlib.sha256(path.read_bytes()).hexdigest()
 
@@ -290,9 +333,12 @@ def test_policy_review_reuses_sealed_video_and_links_yolox_tgs(
 
     def fake_policy_archive(**kwargs) -> list[int]:
         shutil.copyfile(kwargs["source_archive"], kwargs["destination_archive"])
-        return [4489 * 800 * 600, *([0] * 8)]
+        assert kwargs["valid_fov_mask"].is_file()
+        return [4489 * 800 * 600, *([0] * 9)]
 
     monkeypatch.setattr(policy_review_module, "build_policy_mask_archive", fake_policy_archive)
+    valid_fov_mask = tmp_path / "valid-fov-mask.png"
+    Image.new("L", (800, 600), color=255).save(valid_fov_mask)
     result_root = seal_vegetation_policy_review(
         base_lab_root=base_root,
         mission_policy_path=REPOSITORY_ROOT
@@ -300,6 +346,7 @@ def test_policy_review_reuses_sealed_video_and_links_yolox_tgs(
         provider_label_map_path=REPOSITORY_ROOT
         / "config/perception/lab-v1-vegetation-provider-label-map-v1.json",
         m49_tgs_full_shadow_root=tmp_path / "sealed-tgs",
+        valid_fov_mask_path=valid_fov_mask,
         output_root=tmp_path / "results",
         created_at_utc="2026-08-28T08:00:00+00:00",
     )
@@ -312,8 +359,9 @@ def test_policy_review_reuses_sealed_video_and_links_yolox_tgs(
     assert route["taxonomy"]["schema_version"] == (
         "missioncore.lab-v1-terrain-policy-taxonomy/v1"
     )
-    assert len(route["taxonomy"]["classes"]) == 9
+    assert len(route["taxonomy"]["classes"]) == 10
-    assert len(manifest["artifacts"]) == 79
+    assert route["valid_fov"]["outside_valid_fov_class_id"] == 9
+    assert len(manifest["artifacts"]) == 80
     assert manifest["authority"]["commands_enabled"] is False
     assert manifest["decision"]["multilayer_policy_review_ready"] is True