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# Project decisions

- Use a bounded DOTA RGB detection subset and a region-balanced OpenEarthMap segmentation subset.
- Keep academic-only sources explicit; loader default remains commercially compatible.
- Keep ontology classes without reliable labels at zero coverage rather than synthesizing annotations.
- Visual review records identify all preview sheets and bind to the audited corpus content hash.
- For the model build, use separate YOLO26n-obb and YOLO26n-sem pretrained components unless measured baselines disprove the fit; expose only corpus-supported canonical classes.
- Keep model dependencies separate from the dataset-preparation lockfile. Current host is CPU-only; default local gates and training are explicitly time-bounded, with approval required before switching compute.
- Fixed evaluation uses 32 detection plus 20 segmentation validation tiles; the remaining 68/21 tiles are selection-validation only. Records are disjoint, not scenes; all six segmentation validation regions and 24 detection scenes appear in quick evaluation.
- Detection metrics require a shared coordinate frame: corpus positive `obb_polygon` is source-image coordinates, but ignored `clipped_polygon` is tile-local. Translate one side using `tile_coordinates` before evaluation; preserve oriented geometry.
- Threshold calibration checks detection sample IDs against the fixed manifest, not just caller-provided provenance strings. Segmentation class 15 has only one validation tile, reserved for quick evaluation, so no reliable selection-validation threshold is available.
- Loader approval permits CRLF/LF equivalence only for JSON/JSONL; image/mask bytes and missing approved hashes remain strict.
- DOTA v1.5 annotations retain 16 source categories, not 15. Map pretrained source classes by name and expose only the six supported canonical detection classes.
- Include the only validation segmentation tile with bare-ground pixels (canonical ID 15) in fixed quick evaluation; report the sparse evidence explicitly.
- Keep model library settings in the worktree via `YOLO_CONFIG_DIR` and select `CPUExecutionProvider` explicitly for ONNX inference.
- Task 3 official checkpoints are pinned to Ultralytics assets v8.4.0 and SHA-256 in `training/model_factory.py`; Ultralytics 8.4.185 supports task `semantic`, not instance-mask `segment`.
- Official DOTA checkpoint labels contain spaces (for example `small vehicle`); normalize spelling to DOTA hyphens before name-based transfer. Its 15 classes lack only `container-crane` from the 16-class private target. Fresh initialization/row transfer belongs only to later adapted models, never the pretrained baseline.
- ADE baseline uses only exact `building`, `road`, and `water` aliases. IDs 13–16 remain unavailable; do not label three-alias mIoU as a seven-class result or invent broad land-cover aliases from `tree`, `field`, `earth`, or `land`.
- Keep AP on the confidence >=0.001/max_det=300 prediction pool, separate from selection-calibrated operating metrics. Geometry caching is evaluation-local and preserves oriented matching and ignored regions.
- The baseline CLI supervises a spawned worker with a hard 300-second limit including model loading; in-process callers receive cooperative budget checks. Raw detector outputs and compressed original ADE class maps are retained alongside 16 comparable previews and their hashes.

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	# Project decisions

	- Use a bounded DOTA RGB detection subset and a region-balanced OpenEarthMap segmentation subset.
	- Keep academic-only sources explicit; loader default remains commercially compatible.
	- Keep ontology classes without reliable labels at zero coverage rather than synthesizing annotations.
	- Visual review records identify all preview sheets and bind to the audited corpus content hash.
	- For the model build, use separate YOLO26n-obb and YOLO26n-sem pretrained components unless measured baselines disprove the fit; expose only corpus-supported canonical classes.
	- Keep model dependencies separate from the dataset-preparation lockfile. Current host is CPU-only; default local gates and training are explicitly time-bounded, with approval required before switching compute.
	- Fixed evaluation uses 32 detection plus 20 segmentation validation tiles; the remaining 68/21 tiles are selection-validation only. Records are disjoint, not scenes; all six segmentation validation regions and 24 detection scenes appear in quick evaluation.
	- Detection metrics require a shared coordinate frame: corpus positive `obb_polygon` is source-image coordinates, but ignored `clipped_polygon` is tile-local. Translate one side using `tile_coordinates` before evaluation; preserve oriented geometry.
	- Threshold calibration checks detection sample IDs against the fixed manifest, not just caller-provided provenance strings. Segmentation class 15 has only one validation tile, reserved for quick evaluation, so no reliable selection-validation threshold is available.
	- Loader approval permits CRLF/LF equivalence only for JSON/JSONL; image/mask bytes and missing approved hashes remain strict.
	- DOTA v1.5 annotations retain 16 source categories, not 15. Map pretrained source classes by name and expose only the six supported canonical detection classes.
	- Include the only validation segmentation tile with bare-ground pixels (canonical ID 15) in fixed quick evaluation; report the sparse evidence explicitly.
	- Keep model library settings in the worktree via `YOLO_CONFIG_DIR` and select `CPUExecutionProvider` explicitly for ONNX inference.
	- Task 3 official checkpoints are pinned to Ultralytics assets v8.4.0 and SHA-256 in `training/model_factory.py`; Ultralytics 8.4.185 supports task `semantic`, not instance-mask `segment`.
	- Official DOTA checkpoint labels contain spaces (for example `small vehicle`); normalize spelling to DOTA hyphens before name-based transfer. Its 15 classes lack only `container-crane` from the 16-class private target. Fresh initialization/row transfer belongs only to later adapted models, never the pretrained baseline.
	- ADE baseline uses only exact `building`, `road`, and `water` aliases. IDs 13–16 remain unavailable; do not label three-alias mIoU as a seven-class result or invent broad land-cover aliases from `tree`, `field`, `earth`, or `land`.
	- Keep AP on the confidence >=0.001/max_det=300 prediction pool, separate from selection-calibrated operating metrics. Geometry caching is evaluation-local and preserves oriented matching and ignored regions.
	- The baseline CLI supervises a spawned worker with a hard 300-second limit including model loading; in-process callers receive cooperative budget checks. Raw detector outputs and compressed original ADE class maps are retained alongside 16 comparable previews and their hashes.