ArticleScientific reports2021
Towards global flood mapping onboard low cost satellites with machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Foundation Models for Remote Sensing Semantic Segmentation: A Review of Architectures, Adaptations, and Prospects.Sensors (Basel, Switzerland) · 2026Review
- STTORM-CD low-demand and high-impact disaster monitoring onboard satellites using change detection.Scientific reports · 2026Article
- Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation.Sensors (Basel, Switzerland) · 2025Article
- DeepFlood for Inundated Vegetation High-Resolution Dataset for Accurate Flood Mapping and Segmentation.Scientific data · 2025Article
- A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment.Sensors (Basel, Switzerland) · 2024Review
- Global flood extent segmentation in optical satellite images.Scientific reports · 2023Article
- Semantic segmentation of methane plumes with hyperspectral machine learning models.Scientific reports · 2023Article
- Development of High-Precision Urban Flood-Monitoring Technology for Sustainable Smart Cities.Sensors (Basel, Switzerland) · 2023Article
- In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery.Scientific reports · 2023Article
- CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.Scientific data · 2022Article
- RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.Scientific reports · 2022Article
- An object-based sparse representation model for spatiotemporal image fusion.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.