林彥廷
Lin, Yan TingYan‑Ting Lin received a Ph.D. degree in Civil Engineering from National Taiwan University, Taipei, Taiwan, in 2019., He is currently an Assistant Professor at National Chiayi University, Chiayi, Taiwan.
- Ph.D., Department of Civil Engineering, National Taiwan University
- M.Sci., Department of Civil Engineering, National Taiwan University
- B.Sci., Department of Civil and Water Resources Engineering, National Chiayi University
Experience- Project Assistant Researcher (Ph.D.), AI Center, National Center for Research on Earthquake Engineering (2020.02-2022.07)
- Adjunct Assistant Professor, Department of Civil and Construction Engineering, National Taiwan University of Science and Technology (2021.07-2022.07)
- Postdoctoral Research Fellow, National Chung Hsing University (2019.04-2019.08)
- Visiting Scholar, Oak Ridge National Laboratory, USA (2017.03-2017.10)
Disciplines- Civil Engineering
- Geomorphology
- Geoinformatics (GIS)
Areas of Interest
Flood Modeling
Image Processing
Artificial Intelligence (AI)
Geomorphology Surveying
Integrated Spatial Data Analysis
UAV Image Processing & Application
Autonomous Facility Inspection Using UAV ImagesIn this study, an autonomous aerial platform with a functional multipoint patrol module is developed to acquire images of inspected targets. It integrates a high-definition digital surface model (DSM) and a route-searching algorithm to optimize flying route planning. The collected unmanned aerial vehicle (UAV) images are then subjected to a matching technique that automatically detects exposure positions and corrects image distortions. Finally, target facilities are extracted from multitemporal images using object detection techniques so that the status of the inspected targets can be tracked and evaluated. ...
In this study, a modern space remote sensing technology , InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. ...
CCTV Image Analysis
Automatic Water-Level DetectionMonitoring of the water level of reservoirs, rivers, and lakes is an essential task for hydraulic facility management and disaster mitigation. Nowadays, although automated instruments for water-level detection have been widely applied, their reliability and robustness still need to be further improved. On the other hand, surveillance cameras are typically available at major rivers and hydraulic facilities and could provide opportune field observations of the water level. In this study, an automatic water-level detection approach based on single-camera images is developed. ...
Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. ...
River Morphology
Improving River Stage Forecast by Bed Reconstruction in Sinuous BendsBed topography in river bends is highly non-uniform as a result of the spiral motion of fluid and sediment transports related to channel curvature. To grasp a full understanding of geomorphology and hydrology in natural river bends, detailed bed topography data are necessary, but are usually not of high enough quality and so require further interpolation for sophisticated studies. In this paper, an algorithm is proposed that is particularly suited to bathymetry interpolation in rivers with apparent bends. ...
