Reference : Towards global flood mapping onboard low cost satellites with machine learning
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
Physical, chemical, mathematical & earth Sciences : Space science, astronomy & astrophysics
Engineering, computing & technology : Computer science
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/46697
Towards global flood mapping onboard low cost satellites with machine learning
English
Mateo‑Garcia, Gonzalo [Universidad de Valencia, Valencia, Spain]
Veitch‑Michaelis, Joshua [Liverpool John Moores University]
Smith, Lewis [University of Oxford, Oxford, UK]
Oprea, Silviu [University of Edinburgh, Edinburgh, UK]
Schumann, Guy [University of Bristol, Bristol, UK]
Gal, Yarin [University of Oxford, Oxford, UK]
Baydin, Atılım Güneş [University of Oxford, Oxford, UK]
Backes, Dietmar mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
31-Mar-2021
Scientific Reports
Nature Publishing Group
11
7249 (2021)
Yes
International
2045-2322
London
United Kingdom
[en] Earth Observation ; Cubsat ; Flood detection ; Edge processing ; Machine Learning ; Hyperspectral Imaging
[en] Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites— ’CubeSats’ are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products. The ESA’s recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach.
European Space Agency - ESA, Phi-Lab ; Frontiers Development Lab, FDL-Europe ; Intel Corporation ; Google LLC ; University of Oxford
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/46697
10.1038/s41598-021-86650-z
https://www.nature.com/articles/s41598-021-86650-z

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