Development of a Database Based on AI and Open-Source Data for Seismic Loss Estimation in Data-Deficient Regions
- 주제(키워드) Time-history analysis , Open-source data , GIS , Mask R-CNN , eXtreme gradient boosting (XGBoost)
- 주제(DDC) 690
- 발행기관 아주대학교 일반대학원
- 지도교수 문성곤
- 발행년도 2025
- 학위수여년월 2025. 8
- 학위명 석사
- 학과 및 전공 일반대학원 건설시스템공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000035123
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Establishing a building database for city-level earthquake simulations remains challenging due to missing building information. To address this issue, this study proposes an integrated framework that systematically and efficiently collects missing data using artificial intelligence technologies and multiple open-source datasets. The framework was validated using 222 buildings in the Dasan-dong area of Gyeonggi Province, Korea. To implement the framework, 568,866 data points were gathered from various open-source platforms. The building area and construction year were measured using Mask R-CNN while building height and the number of floors were estimated using a vertical-edge-based method. Structural types and building usage were classified using eXtreme Gradient Boosting (XGBoost), thereby constructing a comprehensive seismic building database. Validation confirmed the framework’s robustness: Mask R-CNN detected 99.1% of footprints, with 88.6% of areas and 97.7% of height estimates within 20% relative error and exact floor counts for 84.5% of buildings; XGBoost achieved macro-F1 scores of 0.945 for usage and 0.697 for structural type; and R2D earthquake simulations (Mw 5–8) based on the generated database deviated from ground-truth total repair-cost loss ratios by only 7.07% on average relative error. The proposed framework effectively supplements missing building information at the city level, providing a crucial foundation for disaster preparedness and enhancing urban resilience.
more목차
1. Introduction 1
2. Reviews and method justification 4
2.1 Estimation of building area 4
2.2 Height and floor determination 6
2.3 Estimation of building types and structural types 8
3. Research method 9
3.1 Study area and data collection 10
3.2 Building area estimation 13
3.3 Building height/floor algorithm and construction year 15
3.4 Building and structural types and data conversion 18
4. Evaluation and analysis 20
4.1 Building area 20
4.2 Building heights and floors 24
4.3 Building types 27
4.4 Building structural types 30
5. Earthquake simulation and results 32
6. Summarized findings and conclusion 40
Reference 42
국문 요약 47
