olliestephenson/dpm-rnn-public
Damage mapping with deep learning on satellite data
This project helps disaster response and monitoring teams quickly assess damage from natural hazards like earthquakes, floods, or wildfires using satellite data. It takes a series of radar images from before an event and one image from during the event, then automatically identifies and maps affected areas. The output is a "damage proxy map" that pinpoints where the hazard caused significant changes on the ground, making it easier for analysts to focus their efforts.
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Use this if you are a geospatial analyst, disaster relief coordinator, or researcher experienced with satellite radar data (specifically InSAR coherence time series) and need to generate rapid damage assessments after a natural disaster.
Not ideal if you lack familiarity with Synthetic Aperture Radar (SAR) or Interferometric SAR (InSAR) data processing, as you will need to prepare your own coherence image stacks.
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38
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Language
Python
License
MIT
Category
Last pushed
Jul 30, 2021
Commits (30d)
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