MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones
arXiv:2606.24263v1 Announce Type: cross Abstract: Microwave satellite imagery plays a crucial role in monitoring tropical cyclone precipitation and intensity worldwide, but suffers from long revisit times, potentially missing rapid storm evolution phases. While this raises the need for an interpolation method, it is made challenging by the high level of heterogeneity of microwave data coming from different instruments. In this work, we introduce the first generative model that can be applied to multiple geospatial sources that change across samples, occur at irregular time intervals, are misal
The increasing sophistication of generative AI models and the critical need for more accurate and timely climate monitoring are converging to produce solutions like MotifGen.
This development allows for better spatiotemporal analysis of environmental data, which is crucial for disaster prediction, climate modeling, and resource management.
The ability to interpolate complex, misaligned multi-source satellite imagery via generative models significantly improves data continuity and reduces observational gaps in critical environmental monitoring.
- · Climate scientists
- · Disaster relief organizations
- · Insurance industry
- · Geospatial intelligence companies
- · Traditional satellite data processing methods
- · Regions lacking advanced data analysis capabilities
Improved early warning systems for extreme weather events become possible through enhanced data resolution.
More precise climate models lead to better long-term policy decisions and infrastructure planning.
The generative modeling approach could be generalized to other multi-source, spatiotemporal datasets, accelerating advancements in diverse scientific fields.
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Read at arXiv cs.LG