This study explores AI-driven image classification to expedite damage evaluation by identifying damaged buildings from post-disaster photos much faster than conventional methods, providing a more detailed understanding of structural integrity across affected areas.
Rapid and reliable crack-based visual damage detection after earthquakes is crucial for safe and effective disaster response. Manual inspections are often slow and hazardous for engineers in unstable structures. This study proposes a quadrupedal robotic inspection system for rapid post-earthquake crack-based visual damage detection in reinforced concrete structures. A Unitree Go2 robot equipped with an Intel RealSense D435i RGB-D camera collected a dataset of 3255 annotated crack images from both field and public sources. The YOLOv8n model, trained and deployed on an NVIDIA Jetson AGX Xavier, demonstrated high detection performance in laboratory tests on reinforced concrete specimens, with precision, recall, and mAP@50 values all exceeding 85%. The system provides fast, accurate, and automated structural health assessments, reducing human risk and improving inspection efficiency in hazardous post-disaster environments. Future work will focus on expanding damage detection capabilities and real-world deployment.
Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies.
This study proposes a binary deep learning framework to determine whether the bridges or roads in remote sensing data have been damaged, and provides an automated method for image analysis based on remote sensing data.
Landslides triggered by heavy rainfall have become increasingly frequent and severe, creating a need for the rapid and accurate detection of damaged areas for post-disaster response and recovery planning. This study developed a ChangeFormer-based landslide damage detection model using single-channel differenced Normalized Difference Vegetation Index (dNDVI) imagery derived from pre- and post-event Sentinel-2 data. Landslide reference data were used to construct a patch-based training dataset, and model generalization was evaluated in Sancheong-gun and Hapcheon-gun, Gyeongsangnam-do, Republic of Korea, where landslide damage was reported following heavy rainfall in 2025. As available reference data differed between these regions, region-specific validation strategies were applied. In Sancheong-gun, polygon and point reference data were used for quantitative validation. All 12 reference-defined damaged sites were intersected by the model predictions, corresponding to a site-level detection rate of 100%. Point-based assessment showed an increasing distance-based detection rate with increasing positional tolerance, reaching 87.9% within the 80–100 m tolerance range. This result was interpreted as positional agreement between the reference points and predicted damaged areas rather than as overall accuracy. In Hapcheon-gun, where official polygon- and point-based reference data were unavailable, qualitative external validation using drone imagery indicated that the predicted areas were generally consistent with locations interpreted as landslide damage. These results suggest that the proposed framework is effective for the post-disaster spatial assessment of landslide-damaged areas.
The 2023 Turkey-Kahramanmaras earthquake sequence caused damage to cities along the East Anatolian Fault. This study focuses on Iskenderun, a coastal city in Hatay province. Based on an 1896 historical map, we classify the study area into four land classifications: the old city center, reclaimed land along the coast, former marshland inland, and former canal channels built for drying the marshes. Ataturk Boulevard forms the boundary between the old city center and the reclaimed land. The earthquake triggered lateral spreading and inundation on the reclaimed land, while buildings in the former marshland experienced collapse or damage. A joint team from Turkey, Japan, and Bulgaria conducted field investigations on March 30th and from October 14th to 16th, 2023, performing visual inspections and in-situ tests including portable dynamic cone penetration testing (PDCPT), multi-channel analysis of surface waves (MASW), and horizontal-to-vertical spectral ratio (HVSR) of microtremor. Strong-motion records indicated a peak ground acceleration of 290 cm/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {s}^2$$\end{document}, and the response spectrum exceeded the DD-2 design level, the second-highest earthquake motion level defined in the Turkish Building Earthquake Code. Visual inspections identified ground-related damage in the reclaimed land and along Ataturk Boulevard, and structural damage in the former marshland. PDCPT and MASW at a site on the reclaimed land showed shear wave velocities of 100–140 m/s at the surface, increasing to 185 m/s at 10 m depth, consistent with the lateral spreading observed there. HVSR measurements on the reclaimed land showed predominant frequencies below 1 Hz, while those toward the old city center showed higher predominant frequencies, suggesting spatial variation in the depth of the dominant impedance contrast. The field investigation documented land-class-dependent damage patterns and spatial variation in HVSR characteristics that is consistent with them. The PDCPT and MASW measurements characterize one site on the reclaimed land. At a site on the reclaimed land, shear wave velocities were 100–140 m/s near the surface, while HVSR measurements across the reclaimed land showed predominant frequencies below 1 Hz; the reclaimed land experienced lateral spreading and inundation. The HVSR predominant frequency increased from the northwest to the southeast, and the spatial distribution of HVSR amplitude above 1 Hz showed a low-amplitude zone that spatially overlapped with the area of ground-related damage; this association is interpreted as a working hypothesis rather than a demonstrated causal relationship.
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