The use of social media data in disaster and crisis management is increasing rapidly. Particularly in connection to flooding events, water quality issues in ponds/lakes, urban flooding, and drainage problem, etc. Geo-referenced images shared by citizens can provide situational awareness to emergency responders, as well as assist with financial loss assessment, giving information that would otherwise be very hard to collect through conventional sensors or remote sensing products. Discussion about such events can also be found on various social media platforms. Further, recent advances in computer vision and deep learning can perhaps support the automated analysis of these data. In this problem, software/ algorithm to be developed focusing on ground-level images taken by humans. Considering distinct datasets from different sources, the algorithm of the developed mobile app should be able to categorize water-related problems at different administrative. Further, the mobile app should serve as a valuable tool for the administrators for planning and managing water-related problems.
water-disaster management app
Solving water-related problems through computer vision,deep learning and social media analytics thereby empowering quick disaster responses
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