Awesome

The research paper summarizing the corresponding benchmark and associated solutions can be found here : Deep Learning for Understanding Satellite Imagery: An Experimental Survey

crowdAI Mapping Challenge : Baseline

This repository contains the details of implementation of the Baseline submission using Mask RCNN which obtains a score of [AP(IoU=0.5)=0.697 ; AR(IoU=0.5)=0.479] for the crowdAI Mapping Challenge.

Installation

git clone https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
cd crowdai-mapping-challenge-mask-rcnn
# Please ensure that you use python3.6
pip install -r requirements.txt
python setup.py install

Notebooks

Please follow the instructions on the relevant notebooks for the training, prediction and submissions.

Training
Prediction and Submission (pre-trained weights for baseline submission included)

Results

sample_predictions

Citation

@article{mohanty2020deep, 
    title={Deep Learning for Understanding Satellite Imagery: An Experimental Survey}, 
    author={Mohanty, Sharada Prasanna and Czakon, Jakub and Kaczmarek, Kamil A and Pyskir, Andrzej and Tarasiewicz, Piotr and Kunwar, Saket and Rohrbach, Janick and Luo, Dave and Prasad, Manjunath and Fleer, Sascha and others}, 
    journal={Frontiers in Artificial Intelligence}, 
    volume={3}, 
    year={2020}, 
    publisher={Frontiers Media SA}
}

@misc{crowdAIMappingChallengeBaseline2018,
    author = {Mohanty, Sharada Prasanna},
    title = {CrowdAI Mapping Challenge 2018 : Baseline with Mask RCNN},
    year = {2018},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn}},
    commit = {bac1cf19adbc9d078122c6933da6f808c4ee590d}
}

Acknowledgements

This repository heavily reuses code from the amazing tensorflow Mask RCNN implementation by @waleedka. Many thanks to all the contributors of that project. You are encouraged to checkout https://github.com/matterport/Mask_RCNN for documentation on many other aspects of this code.

Author

Sharada Mohanty sharada.mohanty@epfl.ch