Note: Portions of this blog post first appeared on the Maxar blog.
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Ready to try using satellite imagery, AI and cloud computing to build machine learning models to help coastal communities become more resilient to the effects of climate change?
Today, nearly 75 percent of the world’s population lives within 50 kilometers of the ocean. Coastal zones host critical ecosystems, infrastructure and economic assets. So, it’s of growing concern that these stretches of land are increasingly vulnerable to the dramatic effects of climate change. Our parent company, Maxar Intelligence, is partnering with EY and Microsoft on the latest EY Open Science Data Challenge, which offers university students and early-career professionals with the opportunity to use AI, Maxar’s high-resolution satellite imagery and Microsoft’s Planetary Computer’s Hub environment to help build a sustainable future for society and the planet.
In Phase 1, participants are building machine learning models to detect damaged and undamaged buildings after tropical storms. The top entrants from Phase 1 will move on to Phase 2, which asks them to create a practical disaster response plan describing how their models will be used in practice and the value they offer for disaster response. The awards ceremony will be held in conjunction with the 2024 IEEE International Geoscience and Remote Sensing Symposium (GARSS) in Athens, Greece, in July 2024.
Ask the Experts panel session
To help challenge participants get a jumpstart on their projects, an Ask the Experts panel session was recently held. Our very own Vincent Tompkins, Wovenware senior data scientist; and two experts from EY engaged with registrants to answer any questions they may have about the challenge. View the video recording of the session here.
Satellite imagery used in this challenge
Through its Open Data Program, Maxar provided satellite imagery from 2017 Hurricane Maria in Puerto Rico, specifically in the Analysis-Ready Data (ARD) format. This format means the stack of imagery has undergone atmospheric correction, radiometric correction, orthorectification and pan-sharpening. In addition, ARD uses a patented process to align imagery collected on different dates. ARD provides smoother seamlines between image strips and improved alignment of vectors, which in turn yields greater accuracy for feature extraction and change detection. By handling these preprocessing steps, Maxar enables users of ARD to jump straight into their image analysis workflows, unlocking insights from the satellite imagery faster.
