Deep learning for automatic flood mapping from high resolution SAR images

 With the gradual warming of the global climate, natural catastrophes have caused billions of dollars in damage to ecosystems, economies and properties. Along with the damage, the loss of life is a very serious possibility. With the unprecedented growth of the human population, large-scale development activities and changes to the natural environment, the frequency, and intensity of extreme natural events and consequent impacts are expected to increase in the future. 

 To be able to mitigate and to reduce the potential damage of the natural catastrophe, continuous monitoring is required. The collection of data using earth observation (EO) systems has been valuable for tracking the effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.

 Synthetic aperture radar (SAR) imagery provides highly valuable information about our planet that no other technology is capable of. SAR sensors emit their own energy to illuminate objects or areas on Earth and record what’s reflected back from the surface to the sensor. This allows data acquisition day and night since no sunlight is needed. SAR also uses longer wavelengths than optical systems, which gives it the unsurpassed advantage of being able to penetrate clouds, rain, fog and smoke. All of this makes SAR imagery unprecedentedly valuable in sudden events and crisis situations requiring a rapid response.

 In this talk we will be focusing on flood monitoring using our ICEYE SAR images, taking into account multi-satellites, multi-angles and multi-resolutions that are inherent from our constellation and capabilities. We will present the different steps necessary that have allowed us to improve the consistency of our generated flood maps.