Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): a boulder-mapping file, which is the manually digitized outline of boulders. a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. a global-tiles file, which shows all of the image patches within a raster. There are multiple locations/images per planetary body. Structure: .
└── raw_data/
├── earth/
│ └── image_name/
│   ├── shp/
│   │ ├── <image_name>-ROM.shp
│   │ ├── <image_name>-boulder-mapping.shp
│   │ └── <image_name>-global-tiles.shp
│   └── raster/
│     └── <image_name>.tif
├── mars/
│ └── image_name/
│   ├── shp/
│   │ ├── <image_name>-ROM.shp
│   │ ├── <image_name>-boulder-mapping.shp
│   │ └── <image_name>-global-tiles.shp
│   └── raster/
│     └── <image_name>.tif
└── moon/
  └── image_name/
    ├── shp/
    │ ├── <image_name>-ROM.shp
    │ ├── <image_name>-boulder-mapping.shp
    │ └── <image_name>-global-tiles.shp
    └── raster/
      └── <image_name>.tif File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See https://github.com/astroNils and the MLtools repository for more information. Base-RCNN-FPN.yaml (base model setup file). config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). Mars-MoonEarth-v050...yaml (model setup file). log.txt (log during training of the algorithm). model_0055999.pth (model weights at second last saving step) model_0063999.pth (model weights at last saving step) We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See https://github.com/astroNils and the MLtools repository for more information in how-to-use the different files. The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. The shapefile folder is actually empty (it should not be there!). Structure: .
└── preprocessed_inputs/
  ├── json
  ├── pkl
  ├── preprocessing/
  │   ├── train/
  │   │   ├── images
  │   │   └── labels
  │   ├── validation/
  │   │   ├── images
  │   │   └── labels
  │   └── test/
  │       ├── images
  │       └── labels
  └── shp