Machine Learning-Based Predictive Model of Ground Subsidence Risk Using Characteristics of Underground Pipelines in Urban Areas. - Sungyeol Lee; Jaemo Kang; Jinyoung Kim (IEEE Access, 2023)

Efficiency Study of Combined UAS Photogrammetry and Terrestrial LiDAR in 3D Modeling for Maintenance and Management of Fill Dams. - Joonoh Kang; Daljoo Kim; Chulhee Lee; Jaemo Kang; Donggyou Kim (Remote. Sens., 2023)

Urban Ground Subsidence Vulnerability Assessment through Spatial Regression Analysis. - Jaemo Kang; Jinyoung Kim; Myeongsik Kong; Sungyeol Lee (IGARSS, 2024)

Predictive Approaches for Ground Subsidence Hazard Using Machine Learning. - Sungyeol Lee; Jaemo Kang; Jinyoung Kim; Myeongsik Kong (IGARSS, 2024)

Hybrid MLP-CNN-based ground sink susceptibility prediction in urban area using underground pipe map. - Jun Hwan Park; Ji-Beob Kim; Sungyeol Lee; Jaemo Kang; Duhwan Mun (Reliab. Eng. Syst. Saf., 2024)