Published July 21, 2023 | Version 0.0.1
Dataset Open

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"

  • 1. Stanford University, University of Oslo
  • 2. Stanford University
  • 3. Ponoma University
  • 4. Arizona State University
  • 5. Medvedev Consulting
  • 6. Technion Israel Institute of Technology, Stanford University
  • 7. University of Oslo
  • 8. School of Atmospheric Sciences Sun Yat-Sen University
  • 9. Volcanic Basin Energy Research, A.P. Karpinsky Russian Geological Research Institute Saint Petersburg, University of Oslo

Description

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

 

Files

Apr2023-Mars-Moon-Earth-mask-5px.zip

Files (10.3 GB)

Name Size Download all
md5:af7ae52817d0f42cdda9d10260aa49f9
265.1 MB Preview Download
md5:6208aa6f231ae046764b05a8868732c0
655.0 MB Preview Download
md5:ad8592ada710a00ec2cd8e0db8be9896
9.4 GB Preview Download

Additional details

Funding

European Commission
BOULDERING – A Deep Learning approach for boulder detection –The key to understand planetary surfaces evolution and their crater statistics-based ages 101030364