2019 • Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Image and Video Processing; Machine Learning; Flood detection; Small Satellites; Earth Observation; Artificial Intelligence
Abstract :
[en] Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability to automatically identify flooded regions in images. We describe a prototypical flood segmentation system, identifying cloud, water and land, that could be deployed on a constellation of small satellites, performing processing on board to reduce downlink bandwidth by 2 orders of magnitude. We target PhiSat-1, part of the FSSCAT mission, which is planned to be launched by the European Space Agency (ESA) near the start of 2020 as a proof of concept for this new technology.
Veitch-Michaelis, Josh; Liverpool John Moores University
Schumann, Guy; University of Bristol
Gal, Yarin; University of Oxford
Baydin, AtılımGünes; University of Oxford
Backes, Dietmar ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
External co-authors :
yes
Language :
English
Title :
Flood Detection On Low Cost Orbital Hardware
Publication date :
October 2019
Number of pages :
6
Event name :
Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
