| --- |
| pretty_name: GROC |
| task_categories: |
| - object-detection |
| tags: |
| - remote-sensing |
| - geospatial |
| - aerial-imagery |
| - benchmark |
| - object-counting |
| - object-localization |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # GROC |
|
|
| GROC is a geospatial remote-sensing benchmark with paired raster tiles, labels, split files, tile extents, and source map vector data. The data are acquired from PDOK. |
|
|
| ## Dataset Structure |
|
|
| ```text |
| GROC/ |
| rgb/<group>/ RGB image tiles |
| cir/<group>/ Color-infrared image tiles |
| basemap/<group>/ Basemap image tiles |
| dsm/<group>/ Digital surface model tiles |
| lc/<group>/ Land-cover tiles |
| labels/<group>/ Public annotation files for train and validation samples |
| splits/ Train, validation, test, and benchmark split files |
| benchmark/ Benchmark RGB and CIR image tiles |
| gpkg/ Source map vector data as GeoPackage |
| extent.csv Per-tile geospatial bounding boxes |
| ``` |
|
|
| ## Contents |
|
|
| - `rgb`, `cir`, `basemap`, `dsm`, and `lc` each contain 14,499 image tiles. |
| - `labels` contains 12,427 public annotation files. |
| - `splits/train.txt` contains 9,651 samples. |
| - `splits/val.txt` contains 2,070 samples. |
| - `splits/test.txt` contains 2,072 samples. |
| - `splits/benchmark.txt` contains 200 benchmark samples. |
| - `splits/benchmark_low-light.txt` and `splits/benchmark_weather.txt` each contain 100 benchmark samples. |
| - `benchmark/rgb` and `benchmark/cir` each contain 200 benchmark image tiles. |
| - `gpkg/top10nl_Compleet.gpkg` contains 31 vector layers in EPSG:28992. |
|
|
| ## File Naming |
|
|
| Each split file stores sample stems without file extensions. The hosted dataset shards the large image and label folders by group to satisfy Hugging Face's per-directory file limit. The group is the leading `<category>_group_<id>` prefix of the sample stem. |
|
|
| For a sample stem such as: |
|
|
| ```text |
| airport_group_001_feature_10873_x1_y0 |
| ``` |
|
|
| the group is: |
|
|
| ```text |
| airport_group_001 |
| ``` |
|
|
| and the corresponding raster and label paths are: |
|
|
| ```text |
| rgb/airport_group_001/airport_group_001_feature_10873_x1_y0.png |
| cir/airport_group_001/airport_group_001_feature_10873_x1_y0.png |
| basemap/airport_group_001/airport_group_001_feature_10873_x1_y0.png |
| dsm/airport_group_001/airport_group_001_feature_10873_x1_y0.png |
| lc/airport_group_001/airport_group_001_feature_10873_x1_y0.png |
| labels/airport_group_001/airport_group_001_feature_10873_x1_y0.txt |
| ``` |
|
|
| For test samples, the image products are present but label files are not included. |
|
|
| ## Test Labels |
|
|
| This release withholds labels for the samples listed in `splits/test.txt`. The corresponding label files are intentionally excluded from `labels/` to support benchmark-style evaluation, prevent test-set leakage, and keep reported results comparable across submissions. |
|
|
| If you would like to submit testing results or need help using the dataset, please contact jiayi.wang@whu.edu.cn. |
|
|
| ## Cloud and Shadow Synthesis |
|
|
| For users who want to synthesize clouds or shadows for satellite images, please visit [SatelliteCloudGenerator](https://github.com/strath-ai/SatelliteCloudGenerator). |
|
|
| ## Tile Extents and Vector Features |
|
|
| `extent.csv` provides the geospatial extent of each tile: |
|
|
| ```text |
| id,xmin,ymin,xmax,ymax,crs |
| airport_group_001_feature_311_x1_y3,176005.706999999,407660.699000001,176261.706999999,407916.699000001,EPSG:28992 |
| ``` |
|
|
| Use the `id` column to match a tile stem from the raster folders or split files. The `xmin`, `ymin`, `xmax`, and `ymax` columns define the tile bounding box in the coordinate reference system given by `crs`. |
|
|
| The corresponding map vector features can be retrieved directly from `gpkg/top10nl_Compleet.gpkg` by querying layers with the tile bounding box from `extent.csv`. The GeoPackage layers use EPSG:28992 and include point, line, polygon, and multipolygon TOP10NL feature layers such as buildings, roads, water, terrain, rail, relief, places, and functional areas. |
|
|
| Typical workflow: |
|
|
| 1. Read a sample stem from `splits/train.txt`, `splits/val.txt`, `splits/test.txt`, or `splits/benchmark.txt`. |
| 2. Look up the same stem in `extent.csv` to get the patch extent. |
| 3. Use that extent as a bounding box query against `gpkg/top10nl_Compleet.gpkg`. |
| 4. Use the returned vector features together with the raster tile products for geospatial analysis or model training. |
|
|
