Croissant: add rai:hasSyntheticData, prov:wasGeneratedBy; rename dataLimitation -> dataLimitations

yonder.croissant.json

@@ -16,6 +16,7 @@
       "@type": "@vocab"
     },
     "dct": "http://purl.org/dc/terms/",
+    "prov": "http://www.w3.org/ns/prov#",
     "equivalentProperty": "cr:equivalentProperty",
     "examples": {
       "@id": "cr:examples",
@@ -85,7 +86,9 @@
   "rai:dataAnnotationPlatform": "n/a (programmatic, no human annotators)",
   "rai:dataAnnotationAnalysis": "Bounding box quality was spot-checked across 50 randomly sampled waypoints from 10 scenes. Common failure modes: (1) over-segmentation of articulated objects (e.g., a chair's legs become separate boxes from its seat where mesh authoring split them); (2) tight bounding boxes around partially-occluded objects can be visually misleading. These are inherent to mesh-authored semantics and are documented as a known limitation.",
   "rai:dataReleaseMaintenancePlan": "The dataset is released as a static v1.0.0 snapshot. Errata, additional split files, and supplementary metadata may be added under semver patch/minor releases; the rendered NPZ data itself will not be modified post-release. Issues and clarifications are tracked on the HuggingFace Hub repository. After NeurIPS deanonymization, a maintainer contact will be added to the dataset card.",
-  "rai:dataLimitation": "Yonder is a synthetic-only dataset. Performance on Yonder does not transfer to real-world imagery without explicit sim-to-real treatment. The source scenes (HSSD) are biased toward Western residential interiors; transfer to other interior styles, outdoor settings, or real-world drone footage is not validated. Semantic annotations come from HSSD's mesh-authored instance IDs and inherit known limitations (over-segmentation of articulated objects; occasionally misleading boxes around partially-occluded instances).",
+  "rai:dataLimitations": "Yonder is a synthetic-only dataset. Performance on Yonder does not transfer to real-world imagery without explicit sim-to-real treatment. The source scenes (HSSD) are biased toward Western residential interiors; transfer to other interior styles, outdoor settings, or real-world drone footage is not validated. Semantic annotations come from HSSD's mesh-authored instance IDs and inherit known limitations (over-segmentation of articulated objects; occasionally misleading boxes around partially-occluded instances).",
+  "rai:hasSyntheticData": true,
+  "prov:wasGeneratedBy": "Rendered in Habitat-Sim 0.3.x using a simulated Holybro x500v2 quadcopter. Waypoints were sampled from each scene's navmesh with adaptive density; 12 yaw orientations were captured per waypoint across stereo RGB, depth, IR, LiDAR-360, and semantic-segmentation sensors. Rendering is fully deterministic given (scene_id, seed). Source 3D scenes are HSSD (167 scenes). No human subjects, no real imagery, no PII.",
   "rai:dataSocialImpact": "Drone-perspective perception models trained on this dataset could in principle be applied to surveillance, person-identification, or autonomous-targeting systems. Yonder contains no real persons or PII, but the perception capabilities it enables are dual-use. We discourage use of Yonder-trained models for any application that identifies specific real persons or supports lethal-autonomy systems. The CC-BY-NC license also restricts commercial military/surveillance applications by default.",
   "rai:dataBiases": "Scene-distribution bias: all scenes are HSSD residential interiors (kitchens, bedrooms, living rooms with Western design conventions). Underrepresented: institutional/industrial interiors, non-Western residential styles, outdoor or transitional spaces, low-light or unusual-lighting conditions. Object-distribution bias: object categories are skewed toward those well-represented in HSSD's mesh library (furniture, common household objects); rare or specialized objects are underrepresented. Geographic bias: scene authors and 3D-scan subjects are concentrated in North America and Western Europe.",
   "rai:dataUseCases": "Recommended: training drone-perspective perception models with closed-loop validation in the deployment simulator; studying cross-simulator generalization; benchmarking visual-language navigation when paired with a closed-loop evaluator. Discouraged: end-to-end navigation policy training (no expert trajectories provided); reporting fine-tuning gains based solely on Yonder's offline evaluation split. Disallowed: surveillance, biometric identification, or any application that identifies specific real persons.",