Scholarlink

<p>Inland communities face escalating flood risks as residential development continues within regulatory floodplains, Douglas, and Sarpy Counties in eastern Nebraska, an area heavily impacted by the 2019 Midwest floods. We develop a geospatial artificial intelligence (Geo-AI) framework integrating bi-temporal National Agricultural Imagery Program (NAIP) imagery, parcel-level assessment data, building footprints, and flood hazard maps, and apply a Siamese U-Net deep learning model to detect new, demolished, and existing residential structures. Results show a substantial increase in floodplain exposure, with over 2,250 new residential buildings constructed more than 21 times the number of demolitions primarily in high-assessed-value areas, while removals are concentrated in disadvantaged neighborhoods. The model achieved 98% overall accuracy, demonstrating strong capability for detecting building-scale change. These findings highlight growing inequities in flood-risk exposure and demonstrate the value of Geo-AI for spatially explicit flood-risk monitoring, risk-informed planning, and equitable land-use policy development.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Tracking residential development and flood risk in Eastern Nebraska: a Geo-AI imagery change detection approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

查看全文
  • Jiyoung Lee,
  • Jahangeer Jahangeer,
  • Jesse Andrews,
  • Jenny B. Mason,
  • Yunwoo Nam,
  • Zhenghong Tang

摘要

Inland communities face escalating flood risks as residential development continues within regulatory floodplains, Douglas, and Sarpy Counties in eastern Nebraska, an area heavily impacted by the 2019 Midwest floods. We develop a geospatial artificial intelligence (Geo-AI) framework integrating bi-temporal National Agricultural Imagery Program (NAIP) imagery, parcel-level assessment data, building footprints, and flood hazard maps, and apply a Siamese U-Net deep learning model to detect new, demolished, and existing residential structures. Results show a substantial increase in floodplain exposure, with over 2,250 new residential buildings constructed more than 21 times the number of demolitions primarily in high-assessed-value areas, while removals are concentrated in disadvantaged neighborhoods. The model achieved 98% overall accuracy, demonstrating strong capability for detecting building-scale change. These findings highlight growing inequities in flood-risk exposure and demonstrate the value of Geo-AI for spatially explicit flood-risk monitoring, risk-informed planning, and equitable land-use policy development.