Detecting and monitoring landslides are hot topics in remote sensing community, particularly with the development of remote sensing technologies and the significant progress of computer vision. To the best of our knowledge, no study focused on deep learning-based methods for landslide detection on hyperspectral images. We proposes a deep learning framework with constraints to detect landslides on hyperspectral image. The framework consists of two steps. First, a deep belief network is employed to extract the spectral–spatial features of a landslide. Second, we insert the high-level features and constraints into a logistic regression classifier for verifying the landslide. Experimental results demonstrated that the framework can achieve higher overall accuracy when compared to traditional hyperspectral image classification methods. The precision of the landslide detection on the whole image, obtained by the proposed method, can reach 97.91%, whereas the precision of the linear support vector machine, spectral information divergence, and spectral angle match are 94.36%, 84.50%, and 86.44%, respectively. Also, this article reveals that the high-level feature extraction system has a significant potential for landslide detection, especially in multi-source remote sensing.

This article presents a semi-automated method to extract the lane features along the curved roads from mobile laser scanning (MLS) point clouds. The proposed method consists of four steps. After data pre-processing, a road edge detection algorithm is performed to distinguish road curbs and extract road surfaces. Then, textual and directional road markings such as arrows, symbols, and words, to inform drivers in necessary cases, are detected by intensity thresholding and conditional Euclidean clustering algorithms. Furthermore, lane markings are extracted by local intensity analysis and distance thresholding methods according to road design standards, because they are more regular along the road. Finally, centerline points on lanes are estimated based on the coordinates of extracted lane markings. Our method shows strong feasibility and robustness when creating high-definition (HD) maps from MLS data, by increasing the number of blocks in the curve and the distance threshold control in curved lane centerline extraction. Quantitative evaluations show that the average recall, precision, and F1-score obtained from four datasets for road marking extraction are 93.87%, 93.76%, and 93.73%, respectively. The generated lane centerlines are evaluated by overlaying them on manually labeled reference buffers from 4 cm resolution orthoimagery. The comparative study indicates that the proposed methods can achieve higher accuracy and robustness than most state-of-the-art methods.

This paper recognizes the research gaps and difficulties in generating transition lines (the paths that pass through a road intersection) in road intersections from mobile laser scanning (MLS) point clouds. The proposed method contains three modules: road surface detection, lane marking extraction, and transition line generation. First, the points covering the road surface are extracted using the voxel-based upward growing and the improved region growing. Then, lane markings are extracted and identified according to the multi-thresholding and the geometric filtering. Finally, transition lines are generated through a combination of the lane node structure generation algorithm and the cubic Catmull-Rom spline algorithm. The experimental results demonstrate that transition lines can be successfully generated for both T- and cross-intersections with promising accuracy. In the validation of lane marking extraction using the manually interpreted lane marking points, the method can achieve average precision, recall, and F₁-score of 90.80%, 92.07%, and 91.43%, respectively. The success rate of transition line generation is 96.5%. Furthermore, the buffer-overlay-statistics (BOS) method validates that the proposed method can generate lane centerlines and transition lines within 20-cm-level localization accuracy from the MLS point clouds.