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Yan‑Ting Lin received a Ph.D. degree in Civil Engineering from National Taiwan University, Taipei, Taiwan. He is currently an Assistant Researcher with the NCREE-NTUCE Joint Artificial Intelligence Research Center, National Center for Research on Earthquake Engineering, Taipei, Taiwan. His research focus is on geographic information systems for spatial monitoring, high-performance machine learning approaches for image analysis, and remote sensing data processing.
Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. 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. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.
Yan-Ting Lin; Yi-Keng Chen; Kuo-Hsin Yang; Chuin-Shan Chen; Jen-Yu Han. Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment. Applied Sciences 2021, 11, 7289 .
AMA StyleYan-Ting Lin, Yi-Keng Chen, Kuo-Hsin Yang, Chuin-Shan Chen, Jen-Yu Han. Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment. Applied Sciences. 2021; 11 (16):7289.
Chicago/Turabian StyleYan-Ting Lin; Yi-Keng Chen; Kuo-Hsin Yang; Chuin-Shan Chen; Jen-Yu Han. 2021. "Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment." Applied Sciences 11, no. 16: 7289.
Sophisticated flood simulation in urban areas is a challenging task due to the difficulties in data acquisition and model verification. This study incorporates three rapid-growing technologies, i.e. volunteered geographic information (VGI), unmanned aerial vehicle (UAV), and computational flood simulation (CFS) to reconstruct the flash flood event occurred in 14 June 2015, GongGuan, Taipei. The high-resolution digital elevation model (DEM) generated by a UAV and the real-time VGI photos acquired from social network are served to establish and validate the CFS model, respectively. The DEM data are resampled based on two grid sizes to evaluate the influence of terrain resolution on flood simulations. The results show that flood scenario can be more accurately modelled as DEM resolution increases with better agreement between simulation and observation in terms of flood occurrence time and water depth. The incorporation of UAV and VGI lower the barrier of sophisticated CFS and shows great potential in flood impact and loss assessment in urban areas.
Yuan-Fong Su; Yan-Ting Lin; Jiun-Huei Jang; Jen-Yu Han. Using unmanned aerial vehicle and volunteered geographic information to sophisticate urban flood modelling. 2020, 2020, 1 -22.
AMA StyleYuan-Fong Su, Yan-Ting Lin, Jiun-Huei Jang, Jen-Yu Han. Using unmanned aerial vehicle and volunteered geographic information to sophisticate urban flood modelling. . 2020; 2020 ():1-22.
Chicago/Turabian StyleYuan-Fong Su; Yan-Ting Lin; Jiun-Huei Jang; Jen-Yu Han. 2020. "Using unmanned aerial vehicle and volunteered geographic information to sophisticate urban flood modelling." 2020, no. : 1-22.
Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. 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 (
Yan-Ting Lin; Ming-Der Yang; Jen-Yu Han; Yuan-Fong Su; Jiun-Huei Jang. Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information. Remote Sensing 2020, 12, 706 .
AMA StyleYan-Ting Lin, Ming-Der Yang, Jen-Yu Han, Yuan-Fong Su, Jiun-Huei Jang. Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information. Remote Sensing. 2020; 12 (4):706.
Chicago/Turabian StyleYan-Ting Lin; Ming-Der Yang; Jen-Yu Han; Yuan-Fong Su; Jiun-Huei Jang. 2020. "Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information." Remote Sensing 12, no. 4: 706.
Yan-Ting Lin; Yi Chun Lin; Jen-Yu Han. Automatic water-level detection using single-camera images with varied poses. Measurement 2018, 127, 167 -174.
AMA StyleYan-Ting Lin, Yi Chun Lin, Jen-Yu Han. Automatic water-level detection using single-camera images with varied poses. Measurement. 2018; 127 ():167-174.
Chicago/Turabian StyleYan-Ting Lin; Yi Chun Lin; Jen-Yu Han. 2018. "Automatic water-level detection using single-camera images with varied poses." Measurement 127, no. : 167-174.
Bed 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. The thalweg and the two banks are used as geographical features to ensure that a concave cross-sectional bed-form can be found in bend geometry, while linear interpolations are conducted in the in accordance with secondary and main stream currents, respectively. In comparison with conventional spatial interpolation methods, the proposed algorithm is validated to ensure better performance in generating smooth and accurate bed topography in channel bends, which results in better predictions of river stage by 2D hydrodynamic simulation in practical field tests.
Yan Ting Lin; Wei-Bo Chen; Yuan-Fong Su; Jen-Yu Han; Jiun-Huei Jang. Improving river stage forecast by bed reconstruction in sinuous bends. Journal of Hydroinformatics 2018, 20, 960 -974.
AMA StyleYan Ting Lin, Wei-Bo Chen, Yuan-Fong Su, Jen-Yu Han, Jiun-Huei Jang. Improving river stage forecast by bed reconstruction in sinuous bends. Journal of Hydroinformatics. 2018; 20 (4):960-974.
Chicago/Turabian StyleYan Ting Lin; Wei-Bo Chen; Yuan-Fong Su; Jen-Yu Han; Jiun-Huei Jang. 2018. "Improving river stage forecast by bed reconstruction in sinuous bends." Journal of Hydroinformatics 20, no. 4: 960-974.
Road profile extraction and analysis are essential tasks in transportation asset management. Current approaches use vehicle-borne laser sensors in order to precisely measure the variations in elevation along a specific route. However, a complicated sensor mechanism, such as in the mobile light detection and ranging (LiDAR) system, is involved and the resulting quality is compromised owing to multiple factors. In this study, an image-based approach for extracting road profiles is proposed. It requires only a single camera sensor and a low-cost laser module and is capable of collecting road profiles along both the longitudinal and transverse directions. A detailed methodology is first presented in this paper, followed by a simulation evaluation and a case study. The case study illustrates that the quality of the extracted profiles based on the proposed approach achieves millimeter accuracy. Consequently, an accurate and cost-efficient road profile analysis becomes possible when the proposed approach is implemented.
Jen-Yu Han; Aichin Chen; Yan Ting Lin; Jen-Yu Han M.Asce. Image-Based Approach for Road Profile Analyses. Journal of Surveying Engineering 2016, 142, 06015003 .
AMA StyleJen-Yu Han, Aichin Chen, Yan Ting Lin, Jen-Yu Han M.Asce. Image-Based Approach for Road Profile Analyses. Journal of Surveying Engineering. 2016; 142 (1):06015003.
Chicago/Turabian StyleJen-Yu Han; Aichin Chen; Yan Ting Lin; Jen-Yu Han M.Asce. 2016. "Image-Based Approach for Road Profile Analyses." Journal of Surveying Engineering 142, no. 1: 06015003.
Feature conjugation is a major task in modern-day spatial analysis and contributes to efficient integration across multiple data sets. In this study, an efficient approach that utilizes the intensity information provided in most light detection and ranging (LIDAR) data sets for feature conjugation is proposed. First, a two-dimensional (2D) intensity map is generated based on the original intensity-coded LIDAR observables in three-dimensional (3D) space. The 2D map is further transformed into a regularly sampled image, and an image feature detection technique is subsequently applied to identify point conjugations between a pair of intensity maps. Finally, the paired conjugations in the image space are mapped backward into the LIDAR space, and the object coordinates of the conjugate points can be verified and obtained. Based on the numerical results from a real world case study, it is illustrated that by fully exploring the existing spectral information, a reliable feature conjugation across multiple LIDAR data sets can be easily achieved in an efficient and automatic manner.
Jen-Yu Han; Nei-Hao Perng; Yan Ting Lin. Feature Conjugation for Intensity-Coded LIDAR Point Clouds. Journal of Surveying Engineering 2013, 139, 135 -142.
AMA StyleJen-Yu Han, Nei-Hao Perng, Yan Ting Lin. Feature Conjugation for Intensity-Coded LIDAR Point Clouds. Journal of Surveying Engineering. 2013; 139 (3):135-142.
Chicago/Turabian StyleJen-Yu Han; Nei-Hao Perng; Yan Ting Lin. 2013. "Feature Conjugation for Intensity-Coded LIDAR Point Clouds." Journal of Surveying Engineering 139, no. 3: 135-142.