STAC Overflow: Map Floodwater from Radar Imagery Hosted By Microsoft AI for Earth

competition
complete
$20,000

Detect Floods


Winning solutions can help to improve disaster risk management and response around the world by equipping response teams with more accurate and timely flood maps.

Why

Flooding is the most frequent and costly natural disaster in the world. During a flood event, it is critical that governments and humanitarian organizations be able to accurately measure flood extent in near real-time to strengthen early warning systems, assess risk, and target relief. Yet ground measures only measure water height, are spatially limited, and can be expensive to maintain.

The Solution

High resolution synthetic-aperture radar (SAR) imaging has strengthened monitoring systems by providing data in otherwise inaccessible areas at frequent time intervals. By operating in the microwave band of the electromagnetic spectrum, SAR can capture images through clouds, precipitation, smoke, and vegetation, making it especially valuable for flood detection.

The goal of this challenge was to build machine learning algorithms that can map floodwater using Sentinel-1 global SAR imagery along with supplementary data on elevation and permanent water. To support the development of models, a newly updated dataset of satellite images captured between 2016 and 2020 was prepared and labeled by Cloud to Street and made available through Microsoft’s Planetary Computer.

The Results

IoU graph

The top-performing models obtained IoUs above 0.80, demonstrating a significant improvement over the benchmark solution. They were able to capture signal from polarized SAR data across different geographies and vegetations. All winning solutions successfully leveraged the Planetary Computer STAC API to bolster their models using supplementary NASA Digital Elevation Model (NASADEM) elevation data and/or the European Commission’s Joint Research Centre (JRC) global surface water data to learn about a geography’s natural topography.

All of the prize-winning solutions from this competition are linked below and made available on for anyone to use and learn from.


RESULTS ANNOUNCEMENT + MEET THE WINNERS

WINNING MODELS ON GITHUB