/ Machine Learning

Aerial Semantic Segmentation Benchmark

Aerial Semantic Segmentation Benchmark

Aeroscapes

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.

Instructions

Clone the repository

git clone [email protected]: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

GitHub