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Structural integrity is vital for maintaining the safety and longevity of concrete infrastructures such as bridges, tunnels, and walls. Traditional methods for detecting damages like cracks and spalls are labor-intensive, time-consuming,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-22 Saeid Ataei , Saeed Adibnazari , Seyyed Taghi Ataei

Timely assessment of integrity of structures after seismic events is crucial for public safety and emergency response. This study focuses on assessing the structural damage conditions using deep learning methods to detect exposed steel…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Abdullah Turer , Yongsheng Bai , Halil Sezen , Alper Yilmaz

Post-disaster assessments of buildings and infrastructure are crucial for both immediate recovery efforts and long-term resilience planning. This research introduces an innovative approach to automating post-disaster assessments through…

Computer Vision and Pattern Recognition · Computer Science 2025-02-21 Robinson Umeike , Thang Dao , Shane Crawford

Current methods of practice for inspection of civil infrastructure typically involve visual assessments conducted manually by trained inspectors. For post-earthquake structural inspections, the number of structures to be inspected often far…

Computer Vision and Pattern Recognition · Computer Science 2018-05-04 Vedhus Hoskere , Yasutaka Narazaki , Tu Hoang , BillieF Spencer

This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The current practices and research efforts in adopting AI for post-disaster damage…

Computer Vision and Pattern Recognition · Computer Science 2022-08-26 Asim Bashir Khajwal , Chih-Shen Cheng , Arash Noshadravan

Natural disasters ravage the world's cities, valleys, and shores on a regular basis. Deploying precise and efficient computational mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of…

Computer Vision and Pattern Recognition · Computer Science 2022-01-28 Thomas Y. Chen

We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under realistic…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Robinson Umeike , Cuong Pham , Ryan Hausen , Thang Dao , Shane Crawford , Tanya Brown-Giammanco , Gerard Lemson , John van de Lindt , Blythe Johnston , Arik Mitschang , Trung Do

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an…

Computer Vision and Pattern Recognition · Computer Science 2019-10-16 Joseph Z. Xu , Wenhan Lu , Zebo Li , Pranav Khaitan , Valeriya Zaytseva

Classification of the extent of damage suffered by a building in a seismic event is crucial from the safety perspective and repairing work. In this study, authors have proposed a CNN based autonomous damage detection model. Over 1200 images…

Computer Vision and Pattern Recognition · Computer Science 2019-07-19 Dhananjay Nahata , Harish Kumar Mulchandani , Suraj Bansal , G Muthukumar

After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by…

Computer Vision and Pattern Recognition · Computer Science 2020-07-09 Quoc Dung Cao , Youngjun Choe

Building damage detection after natural disasters like earthquakes is crucial for initiating effective emergency response actions. Remotely sensed very high spatial resolution (VHR) imagery can provide vital information due to their ability…

Computer Vision and Pattern Recognition · Computer Science 2025-01-22 Jun Wang

Satellite imagery has played an increasingly important role in post-disaster building damage assessment. Unfortunately, current methods still rely on manual visual interpretation, which is often time-consuming and can cause very low…

Computer Vision and Pattern Recognition · Computer Science 2024-05-09 Irene Alisjahbana , Jiawei Li , Ben , Strong , Yue Zhang

Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of pre- and…

Computer Vision and Pattern Recognition · Computer Science 2022-02-16 Yu Shen , Sijie Zhu , Taojiannan Yang , Chen Chen , Delu Pan , Jianyu Chen , Liang Xiao , Qian Du

This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual network (ResNet)…

Computer Vision and Pattern Recognition · Computer Science 2022-05-05 Yongsheng Bai , Bing Zha , Halil Sezen , Alper Yilmaz

Quick and accurate assessment of the damage state of buildings after natural disasters is crucial for undertaking properly targeted rescue and subsequent recovery operations, which can have a major impact on the safety of victims and the…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Mateusz Żarski , Jarosław Adam Miszczak

Maintaining roadway infrastructure is essential for ensuring a safe, efficient, and sustainable transportation system. However, manual data collection for detecting road damage is time-consuming, labor-intensive, and poses safety risks.…

Computer Vision and Pattern Recognition · Computer Science 2024-10-14 Vung Pham , Lan Dong Thi Ngoc , Duy-Linh Bui

Due to cyclic loading and fatigue stress cracks are generated, which affect the safety of any civil infrastructure. Nowadays machine vision is being used to assist us for appropriate maintenance, monitoring and inspection of concrete…

Computer Vision and Pattern Recognition · Computer Science 2020-09-23 Babloo Kumar , Sayantari Ghosh

Developing a rapid, but also reliable and efficient, method for classifying the seismic damage potential of buildings constructed in countries with regions of high seismicity is always at the forefront of modern scientific research. Such a…

Machine Learning · Computer Science 2022-05-03 Konstantinos Kostinakis , Konstantinos Morfidis , Konstantinos Demertzis , Lazaros Iliadis

Post-hurricane damage assessment is crucial towards managing resource allocations and executing an effective response. Traditionally, this evaluation is performed through field reconnaissance, which is slow, hazardous, and arduous. Instead,…

Computer Vision and Pattern Recognition · Computer Science 2022-09-07 Jimmy Bao

In the aftermath of an earthquake, rapid structural inspections are required to get citizens back in to their homes and offices in a safe and timely manner. These inspections gfare typically conducted by municipal authorities through…

Computer Vision and Pattern Recognition · Computer Science 2018-09-26 Vedhus Hoskere , Yasutaka Narazaki , Tu A. Hoang , Billie F. Spencer
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