Aerial Semantic Segmentation Benchmark
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assets Release dataset. May 25, 2018
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README.md

Aeroscapes

Introduction

The AeroScapes aerial semantic segmentation benchmark comprises of images captured using a commercial drone from an altitude range of 5 to 50 metres. The dataset provides 3269 720p images and ground-truth masks for 11 classes.

aeroscapes_dataset_sample_images

Instructions

Clone the repository

git clone git@github.com:ishann/aeroscapes.git

Download the data

bash download.sh

This results in the following directory

data/
    aeroscapes/
        JPEGImages/
            3269 RGB images.
        SegmentationClass/
            3269 ground-truth segmentation masks.
        Visualizations/
            3269 RGB ground-truth segmentation visualizations.
        ImageSets/
            Training and validation splits for data.
    aeroscapes.tar.gz
        Downloaded file (local reference to avoid need for repeated downloads).

Reference

If you use AeroScapes in your research, please cite the following:

Ensemble Knowledge Transfer for Semantic Segmentation
Ishan Nigam, Chen Huang, Deva Ramanan
Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision

Acknowledgements

We acknowledge the efforts of Autel Robotics in the collection and manual annotation of the dataset.

Questions and Comments

For comments and feedback, contact Ishan Nigam at inigam@cs.cmu.edu.