RarePlanes: Synthetic Data Takes Flight

This repository contains scripts for inspection, preparation and evaluation of the RarePlanes dataset.

The user guide provides a more detailed description of the dataset.

Paper

@misc{shermeyer2020rareplanes,
    title={RarePlanes: Synthetic Data Takes Flight},
    author={Jacob Shermeyer and Thomas Hossler and Adam Van Etten and Daniel Hogan and Ryan Lewis and Daeil Kim},
    year={2020},
    eprint={2006.02963},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Please cite our paper if you find it useful for your research.

Getting Started

Using docker

We highly recommend using the available docker image as some of the dependencies can be strenous to install. You can either pull the image:

docker pull aireverie/rareplanes:latest

or build it locally:

docker build -t rareplanes .

Without docker

Follow the installation guidelines of solaris.

Download data

Instructions are available in the user guide and here.

Models and baselines

We provide all the baseline results and made the trained models available for download here

Create tiles from satellite images

Pre-tiled data can be found in the geojson_aircraft_tiled directory. These data have a size of 512x512 pixels with an overlap of 20% per tile. End users can retile their data if necessary.

For example to tile into 1024x1024 tiles with no overlap:

python tools/create_tiles.py --image_dir datasets/real/train/PS-RGB
                             --geojson_dir datasets/real/train/geojson_aircraft
                             --tile_image_dir datasets/real/train/retiled_images
                             --tile_geojson_dir datasets/real/train/retiled_geojson
                             --tile_size 1024
                             --overlap 0

Create coco files

Using the create_coco_real.py or create_coco_synthetic.py script, the user can select the attribute that they want to use as a category.

For aircraft detection

For example, to create a coco file where each object is labeled as aircraft, the following script is ran:

python tools/create_coco_real.py --image_dir datasets/real/train/PS-RGB_tiled
                            --geojson_dir datasets/real/train/geojson_aircraft_tiled
                             --output_path ./aircraft_real_coco.json
                             
python tools/create_coco_synthetic.py --data_dir datasets/synthetic/
                            --segmentation simple
                            --output_path ./aircraft_synthetic_coco.json

For other attributes

If we want to classify the aircraft by number of engines instead:

python tools/create_coco_real.py --image_dir datasets/real/train/PS-RGB_tiled
                            --geojson_dir datasets/real/train/geojson_aircraft_tiled
                             --output_path ./num_engines_real_coco.json
                            --category_attribute num_engines
                            
python tools/create_coco_synthetic.py --data_dir datasets/synthetic/
                            --segmentation simple
                            --output_path ./num_engines_synthetic_coco.json
                            --category_attribute num_engines

By role

python tools/create_coco_real.py --image_dir datasets/real/train/PS-RGB_tiled
                            --geojson_dir datasets/real/train/geojson_aircraft_tiled
                            --output_path ./role_real_coco.json
                            --category_attribute role
                           
python tools/create_coco_synthetic.py --data_dir datasets/synthetic/
                            --segmentation simple
                            --output_path ./role_synthetic_coco.json
                            --category_attribute role                          

Create custom classes

If we want to identify aircraft based on a unique combination of classes:

python tools/create_custom_classes.py --all_annotations_geojson datasets/real/metadata_full_annotations/RarePlanes_Public_All_Annotations.geojson
                            --geojson_dir datasets/real/train/geojson_aircraft_tiled
                            --output_path datasets/real/train/geojson_aircraft_tiled_custom
                            --category_attributes ['num_engines', 'role', 'propulsion']
                            
                            
python tools/create_coco_real.py --image_dir datasets/real/train/PS-RGB_tiled
                            --geojson_dir datasets/real/train/geojson_aircraft_tiled_custom
                            --output_path ./custom_real_coco.json
                            --category_attribute custom_id
                            --custom_class_lookup_csv datasets/real/train/geojson_aircraft_tiled_custom/custom_class_lookup.csv
                            
                            
python tools/create_coco_synthetic.py --data_dir datasets/synthetic/
                            --segmentation simple
                            --output_path ./custom_synthetic_coco.json
                            --category_attribute custom_id
                            --custom_class_lookup_csv datasets/real/train/geojson_aircraft_tiled_custom/custom_class_lookup.csv

Authors

• Thomas Hossler
• Jake Shermeyer