Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land
  Cover Mapping
Hakan T. Otal Elyse Zavar Sherri B. Binder Alex Greer M. Abdullah Canbaz
Environmental disasters such as floods, hurricanes, and wildfires have
increasingly threatened communities worldwide, prompting various mitigation
strategies. Among these, property buyouts have emerged as a prominent approach
to reducing vulnerability to future disasters. This strategy involves
governments purchasing at-risk properties from willing sellers and converting
the land into open space, ostensibly reducing future disaster risk and impact.
However, the aftermath of these buyouts, particularly concerning land-use
patterns and community impacts, remains under-explored. This research aims to
fill this gap by employing innovative techniques like satellite imagery
analysis and deep learning to study these patterns. To achieve this goal, we
employed FEMA's Hazard Mitigation Grant Program (HMGP) buyout dataset,
encompassing over 41,004 addresses of these buyout properties from 1989 to
2017. Leveraging Google's Maps Static API, we gathered 40,053 satellite images
corresponding to these buyout lands. Subsequently, we implemented five
cutting-edge machine learning models to evaluate their performance in
classifying land cover types. Notably, this task involved multi-class
classification, and our model achieved an outstanding ROC-AUC score of 98.86%