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%

翻译：环境灾害如洪水、飓风和野火日益威胁全球社区，促使各方采取多种减灾策略。其中，财产收购作为一种显著方法，旨在通过政府向自愿出售者购买风险财产并将土地转化为开放空间，以降低未来灾害风险与影响。然而，这些收购后的结果，特别是涉及土地利用模式和社区影响的方面，仍未被充分探索。本研究旨在通过采用卫星影像分析和深度学习等创新技术来填补这一空白。为实现此目标，我们使用了联邦紧急事务管理局（FEMA）的减灾资助计划（HMGP）收购数据集，涵盖1989年至2017年间41,004处收购财产的地址。借助谷歌地图静态API，我们收集了对应这些收购土地的40,053幅卫星影像。随后，我们实施了五种前沿机器学习模型，以评估其在土地覆盖类型分类中的性能。值得注意的是，该任务涉及多类分类，我们的模型取得了高达98.86%的ROC-AUC分数。