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, 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 d

SUBMITTER: Mateo-Garcia G 

PROVIDER: S-EPMC8012608 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature