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 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.

The Φ-Sat-1 Mission: The First On-Board Deep Neural Network Demonstrator for Satellite Earth Observation

“…Moving AI to the edge can have a twofold benefit: 1) it enables new remote sensing techniques and 2) it enables new types of applications such as those requiring minimal latency direct downlink to the final user, or those optimizing the downlink bandwidth by transmitting to ground only useful data or only meta-information [27], [28]. In particular, the deployment of DNNs on-board can help to reduce mission/application bandwidth requirements by filtering out nonuseful data [2], [29].…”

Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional Neural Networks Against an Operational Rule-Based Processing Chain

“…Several studies have shown that the U-Net architecture [40] is able to deliver state-of-the-art results in water segmentation tasks using either Multispectral (e.g. [19], [41]) or SAR data (e.g. [22], [23], [30], [34]).…”

Unsupervised Change Detection of Extreme Events Using ML On-Board

“…Images are taken from the Sentinel-2 multi-spectral imager (MSI) instrument 2 and include the ten highest resolution channels with all channels interpolated to the highest resolution of 10m. Training data are taken from the WorldFloods dataset of [2] locations (Figure 1a), with a total of 233 scenes and a time series of five images per scene. The validation set, consists of Sentinel-2 time series for four classes of disasters: hurricanes, fire burn scars, landslides and floods (Figure 1b).…”

“…There has been considerable recent interest in using machine learning on-board for this processing. Existing work has focused on the deployment of supervised classifiers for applications such as identifying clouds [1] or floods [2]. Supervised learning has a significant drawback: only events of a particular type determined at training time will be flagged and prioritised for downlink, with no generalisation to new event types, imager specifications, and lighting or local features.…”