Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping

TL;DR

This study combines satellite imagery and deep learning to analyze land-use changes following property buyouts for disaster mitigation, providing insights into post-buyout land cover patterns.

Contribution

It introduces a novel approach using satellite imagery and deep learning to assess land cover changes after buyouts, filling a gap in understanding community impacts.

Findings

High classification accuracy with ROC-AUC of 98.86%

Effective use of satellite imagery for land cover analysis

Large dataset of over 40,000 properties analyzed

Abstract

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%