Rapid and Automated Vision-Based Post-Disaster Building Debris Estimation
Publication: Computing in Civil Engineering 2023
ABSTRACT
Debris removal is one of the most expensive and challenging aspects of recovery from disasters. Erroneous debris estimation and delayed cleanup operation can block or slow down access to affected areas, thus putting public health at risk, and seriously hinder effective post-disaster search-and-rescue (SAR) and resource allocation. Existing disaster debris estimation techniques merely produce rough estimates of debris volume with significant errors. Recent research has demonstrated the value of low-cost unmanned aerial vehicles (UAVs) and artificial intelligence (AI) to rapidly collect information and assist in performing damage assessment of the building stock. However, the utility of AI models in debris estimation and classification is still underexplored. To this end, this study aims at enabling the measurement of debris volume and composition in residential structures from the outcome of AI-based damage assessment. Results from scaled experiments indicate that the proposed approach can provide high-fidelity estimation of disaster debris volume and composition.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 60 - 67
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Buildings
- Business management
- Computer programming
- Computing in civil engineering
- Debris
- Disaster preparedness
- Disaster recovery
- Disaster risk management
- Disasters and hazards
- Environmental engineering
- Health hazards
- Pollutants
- Practice and Profession
- Public administration
- Public health and safety
- Residential buildings
- Solid wastes
- Structural engineering
- Structures (by type)
- Wastes
Authors
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