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Advanced Geo-Data Analytics and AI for 3D Flood Mapping to Protect Built Assets.

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Title: Advanced Geo-Data Analytics and AI for 3D Flood Mapping to Protect Built Assets.
Authors: Blay, Jeffrey, Hashemi-Beni, Leila
Source: ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 4/6/2025, Vol. 10 Issue G-2025, p159-164, 6p
Subject Terms: DEEP learning, FLOOD risk, AERIAL photographs, GEOSPATIAL data, ARTIFICIAL intelligence, LIDAR, HURRICANE Matthew, 2016
Geographic Terms: GREENVILLE (N.C.), UNITED States, NORTH Carolina
Abstract: Floods are among the most destructive natural disasters, posing significant risks to human lives and property. This study investigates the impact of Hurricane Matthew on built assets in Greenville, North Carolina, USA in 2016 using an integrated approach that combined floodwater extent mapping, depth estimation, and impact assessment. In particular, our objective is to accurately map and estimate floodwater depth using deep learning techniques combined with aerial imagery and lidar data to assess the extent of flooding's impact on critical infrastructure such as buildings and roads. The pretrained UNET model utilized, achieved high accuracy in mapping flood extent, with a 93% accuracy, while floodwater depth estimates yielded a root mean square error (RMSE) of 0.75, reflecting a deviation of approximately 1ft from field measurements. The results highlighted the severe damage sustained by essential assets, notably Greenville Airport, which experienced significant flooding and disruption. The research results revealed that approximately 32% (415 acres) of developed land, 26% (185) of buildings, and 66% (23 miles) of roads were affected. These findings provide critical insights that can guide policymakers in crafting effective mitigation and adaptation strategies to protect urban areas and essential infrastructure. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:Floods are among the most destructive natural disasters, posing significant risks to human lives and property. This study investigates the impact of Hurricane Matthew on built assets in Greenville, North Carolina, USA in 2016 using an integrated approach that combined floodwater extent mapping, depth estimation, and impact assessment. In particular, our objective is to accurately map and estimate floodwater depth using deep learning techniques combined with aerial imagery and lidar data to assess the extent of flooding's impact on critical infrastructure such as buildings and roads. The pretrained UNET model utilized, achieved high accuracy in mapping flood extent, with a 93% accuracy, while floodwater depth estimates yielded a root mean square error (RMSE) of 0.75, reflecting a deviation of approximately 1ft from field measurements. The results highlighted the severe damage sustained by essential assets, notably Greenville Airport, which experienced significant flooding and disruption. The research results revealed that approximately 32% (415 acres) of developed land, 26% (185) of buildings, and 66% (23 miles) of roads were affected. These findings provide critical insights that can guide policymakers in crafting effective mitigation and adaptation strategies to protect urban areas and essential infrastructure. [ABSTRACT FROM AUTHOR]
ISSN:21949042
DOI:10.5194/isprs-annals-X-G-2025-159-2025