Feature/tile overlap - #1266
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SpatialGridDescriptor carries the tile overlap halo (serde default = zero). Merging requires equal overlaps; mapping/reprojection resets the overlap because grid geometry changes invalidate padding.
SpatialGridDescriptor carries the tile overlap halo (serde default = zero). Merging requires equal overlaps; mapping/reprojection resets the overlap because grid geometry changes invalidate padding.
Add descriptor validation helpers (ensure_no_tile_overlap, ensure_equal_tile_overlap) plus error variants, and apply the rejection in operators that count pixels, manage their own margins, or assume context tile shapes: Onnx, RasterStacker, NeighborhoodAggregate, BandNeighborhoodAggregate, TemporalRasterAggregation, Downsampling, Interpolation, BandFilter, Histogram, ClassHistogram, Statistics, BoxPlot, MeanRasterPixelValuesOverTime.
Pads every enumerated core tile with neighbor data up to the declared halo via the RasterSubQueryAdapter fold machinery; regions beyond the dataset extent stay no-data. Output descriptor declares the overlap. Rejects already-overlapped inputs (v1) and halos larger than one core.
AddTileOverlap pads each enumerated core tile with neighbor data via the RasterSubQueryAdapter fold machinery (no-data beyond the dataset extent); RemoveTileOverlap crops halos symmetrically with a plain stream map. Both register in the processing graph API. The WMS/WCS wrap utility now strips overlap before projection, resampling, and output so sinks never see padded tiles. openapi.json regenerated accordingly.
Take TileOverlap accessor receivers by value (Copy type), document the infallible internal expects, use expect_err in tests.
…ansforms Add core-anchored local pixel bounds (local_core_pixel_bounds / local_total_pixel_bounds) to BaseTile; the CoordinatePixelAccess hub now validates a local-indexed pixel against the local total bounds before addressing the stored (data-corner) grid. Migrate tile_geo_transform to core_geo_transform and make serialized worldfiles / coordinate lookups overlap-aware. Fold the hidden TileInformation::global_pixel_bounds into core_pixel_bounds and rename data_pixel_bounds -> total_pixel_bounds, updating callers across operators and services.
Extend the ONNX model descriptor with overlap metadata (halo in/out + required source overlap) and validate it against the tile at setup. Feed the model a halo-padded region and mask the output back to the core, addressing tiles by core-anchored local pixel bounds. Add a convolution test model and training script exercising the overlap path.
Add an OnnxObjectDetection VectorOperator that runs a raw (pre-NMS) YOLO ONNX detection model on a raster source and emits bounding boxes as a MultiPolygonCollection with class (category) and score (float) columns. - detection_decoder: YOLO box decode (center/corner, objectness) + NMS - onnx_object_detection: operator, init-time size/resolution checks, NHWC tile packing, session run, decode, confidence filter, NMS, pixel->world mapping - test model (test_detection.onnx) + build script, and an end-to-end tokio test
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Here is a brief summary of what I did: