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

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:

airport_group_001_feature_10873_x1_y0

the group is:

airport_group_001

and the corresponding raster and label paths are:

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.

Tile Extents and Vector Features

extent.csv provides the geospatial extent of each tile:

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.