Detecting Roads from Satellite Imagery in the Developing World

Yoni Nachmany, Hamed Alemohammad; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 83-89

Abstract


Advances in computer vision are improving the ability to accurately extract structured information from frequent and high-resolution satellite imagery, shedding light on global challenges and furthering Sustainable Development Goals. While these advances, along with increased availability of high capacity computational resources, result in improved models, lack of diverse training data significantly limits applications of these models to certain geographical regions. We review state-of-the-art models for road detection using satellite imagery, and compare predictions of two models (one trained in Las Vegas, USA and another in Khartoum, Sudan) in Khartoum. This comparison shows the need for regionally trained models using local training data. Finally, we outline a roadmap to use transfer learning and regional models in cities that do not have human verified labels.

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[bibtex]
@InProceedings{Nachmany_2019_CVPR_Workshops,
author = {Nachmany, Yoni and Alemohammad, Hamed},
title = {Detecting Roads from Satellite Imagery in the Developing World},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}