Towards global flood mapping onboard low cost satellites with machine learning

Abstract 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,...

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Autores principales:	Gonzalo Mateo-Garcia, Joshua Veitch-Michaelis, Lewis Smith, Silviu Vlad Oprea, Guy Schumann, Yarin Gal, Atılım Güneş Baydin, Dietmar Backes
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a19d5f1569544e6c8dc25ba67ba5f7eb
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spelling	oai:doaj.org-article:a19d5f1569544e6c8dc25ba67ba5f7eb2021-12-02T14:25:03ZTowards global flood mapping onboard low cost satellites with machine learning10.1038/s41598-021-86650-z2045-2322https://doaj.org/article/a19d5f1569544e6c8dc25ba67ba5f7eb2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86650-zhttps://doaj.org/toc/2045-2322Abstract 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.Gonzalo Mateo-GarciaJoshua Veitch-MichaelisLewis SmithSilviu Vlad OpreaGuy SchumannYarin GalAtılım Güneş BaydinDietmar BackesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gonzalo Mateo-Garcia Joshua Veitch-Michaelis Lewis Smith Silviu Vlad Oprea Guy Schumann Yarin Gal Atılım Güneş Baydin Dietmar Backes Towards global flood mapping onboard low cost satellites with machine learning
description	Abstract 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.
format	article
author	Gonzalo Mateo-Garcia Joshua Veitch-Michaelis Lewis Smith Silviu Vlad Oprea Guy Schumann Yarin Gal Atılım Güneş Baydin Dietmar Backes
author_facet	Gonzalo Mateo-Garcia Joshua Veitch-Michaelis Lewis Smith Silviu Vlad Oprea Guy Schumann Yarin Gal Atılım Güneş Baydin Dietmar Backes
author_sort	Gonzalo Mateo-Garcia
title	Towards global flood mapping onboard low cost satellites with machine learning
title_short	Towards global flood mapping onboard low cost satellites with machine learning
title_full	Towards global flood mapping onboard low cost satellites with machine learning
title_fullStr	Towards global flood mapping onboard low cost satellites with machine learning
title_full_unstemmed	Towards global flood mapping onboard low cost satellites with machine learning
title_sort	towards global flood mapping onboard low cost satellites with machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a19d5f1569544e6c8dc25ba67ba5f7eb
work_keys_str_mv	AT gonzalomateogarcia towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT joshuaveitchmichaelis towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT lewissmith towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT silviuvladoprea towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT guyschumann towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT yaringal towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT atılımgunesbaydin towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning AT dietmarbackes towardsglobalfloodmappingonboardlowcostsatelliteswithmachinelearning
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Towards global flood mapping onboard low cost satellites with machine learning

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