Improving Emergency Response during Hurricane Season using Computer Vision

• URL: http://arxiv.org/abs/2008.07418v2
• Date: Tue, 8 Sep 2020 19:51:37 GMT
• Title: Improving Emergency Response during Hurricane Season using Computer Vision
• Authors: Marc Bosch and Christian Conroy and Benjamin Ortiz and Philip Bogden
• Abstract summary: We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster. We have designed an ensemble of models to identify features including water, roads, buildings, and vegetation from the imagery.
• Score: 0.06882042556551608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. The framework uses data collected before, during, and after the crisis to enable rapid and informed decision making during all phases of disaster response. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster and creates metadata that is transformed into actionable information through web-accessible mapping tools. In particular, we have designed an ensemble of models to identify features including water, roads, buildings, and vegetation from the imagery. We have investigated techniques to bootstrap and reduce dependency on large data annotation efforts by adding use of open source labels including OpenStreetMaps and adding complementary data sources including Height Above Nearest Drainage (HAND) as a side channel to the network's input to encourage it to learn other features orthogonal to visual characteristics. Modeling efforts include modification of connected U-Nets for (1) semantic segmentation, (2) flood line detection, and (3) for damage assessment. In particular for the case of damage assessment, we added a second encoder to U-Net so that it could learn pre-event and post-event image features simultaneously. Through this method, the network is able to learn the difference between the pre- and post-disaster images, and therefore more effectively classify the level of damage. We have validated our approaches using publicly available data from the National Oceanic and Atmospheric Administration (NOAA)'s Remote Sensing Division, which displays the city and street-level details as mosaic tile images as well as data released as part of the Xview2 challenge.

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