[논문]토공현장 디지털맵 시계열 변화분석 프레임워크 기술개발

김용건; 박수열; 김석

doi:10.13161/kibim.2023.13.1.022

토공현장 디지털맵 시계열 변화분석 프레임워크 기술개발
Development of Framework for Digital Map Time Series Analysis of Earthwork Sites 원문보기

Journal of KIBIM = 한국BIM학회논문집, v.13 no.1, 2023년, pp.22 - 32

김용건 (한국교통대학교 철도시설공학과) , 박수열 (한국교통대학교 철도융합시스템공학과) , 김석 (한국교통대학교 철도인프라시스템공학과)

Abstract ▼ AI-Helper

Due to the increased use of digital maps in the construction industry, there is a growing demand for high-quality digital map analysis. With the large amounts of data found in digital maps at earthwork sites, there is a particular need to enhance the accuracy and speed of digital map analysis. To address this issue, our study aims to develop new technology and verify its performance to address non-ground and range mismatch issues that commonly arise. Additionally, our study presents a new digital map analysis framework for earthwork sites that utilizes three newly developed technologies to improve the performance of digital map analysis. Through this, it achieved about 95% improvement in analysis performance compared to the existing framework. This study is expected to contribute to the improvement of the quality of digital map analysis data of earthworks.

주제어

표/그림 (17)

그림 Figure 1. Error in digital map analysis
그림 Figure 2. A framework for time series analysis of digital map
그림 Figure 3. Time series raw data
표 Table 1. Number of points in raw and preprocessed data
그림 Figure 4. Process of ground classification
그림 Figure 5. Visual analysis of clustering algorithms using scikit learn
그림 Figure 6. Range matching and change detection process
그림 Figure 7. Verification methodology of range matching
그림 Figure 8. Verification process of ground classification
그림 Figure 9. Non-ground data of classified by topographic algorithms
표 Table 2. Optimal value of topographic algorithms
표 Table 3. Classification rate by topographic algorithms
그림 Figure 10. Ground data of classified by SMRF and DBSCAN
그림 Figure 11. Verification process of range matching and change analysis framework
표 Table 4. Classification rate by SMRF and DBSCAN
그림 Figure 12. Time series change detection results by framework
표 Table 5. Number of error points in change detection data

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