Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
February 14, 2026˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesOpen Access

From Survey to Action: AI-Driven Severe Damage Mapping

View Full Paper
Ask AI
Bookmark
Share

Authors

KZKai ZhangATAsli TekinCMChiara Mea

Discussion

Loading...

Member takes

Overview

This analysis utilizes AI for rapid structural damage assessment in post-disaster scenarios, indicating improved response times.

Key Points

  • The aim is to develop an AI-based method for swiftly identifying structural damage to enhance post-disaster response.
  • Gathered a dedicated dataset for crack detection in buildings.
  • Employed YOLOv11 for object detection and segmentation on collected case study data.
  • Integrated deep learning and machine learning with a photogrammetric workflow for damage localization.
  • Achieved effective localization of severe damage in 3D based on 2D data.
  • Validated the hybrid approach against ground truth data, demonstrating strong accuracy in damage detection.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/699011932ccff479cfe585f9https://doi.org/10.5194/isprs-archives-xlviii-2-w12-2026-519-2026
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning2024 · 75 citations
  2. 2A Unified Framework for Automated Damage Assessment in Post-Disaster Built Environments Using LiDAR Point Clouds2026
  3. 3AI-Based Image and Data Analysis for Automated Assessment of Residential Damage in Seismic Regions2026
  4. 4Rapid regional assessment of post‐hazard structures and transportation infrastructure using aerial images2025 · 3 citations
  5. 5Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning2026 · 1 citations