Automatic Detection of Forested Landslides: A Case Study in Jiuzhaigou County, China

Abstract

Landslide detection and distribution mapping are essential components of geohazard prevention. For the extremely difficult problem of automatic forested landslide detection, airborne remote sensing technologies, such as LiDAR and optical cameras, can obtain more accurate landslide monitoring data. In practice, however, airborne LiDAR data and optical images are treated independently. The complementary information of the remote sensing data from multiple sources has not been thoroughly investigated. To address this deficiency, we investigate how to use LiDAR data and optical images together to develop an automatic detection model for forested landslide detection. First, a new dataset for detecting forested landslides in the Jiuzhaigou earthquake region is compiled. LiDAR-derived DEM and hillshade maps are used to mitigate the influence of forest cover on the detection of forested landslides. Second, a new deep learning model called DemDet is proposed for the automatic detection of forested landslides. In the feature extraction component of DemDet, a self-supervised learning module is proposed for extracting geometric features from LiDAR-derived DEM. Additionally, a transformer-based deep neural network is proposed for identifying landslides from hillshade maps and optical images. In the data fusion component of DemDet, an attention-based neural network is proposed to combine DEM, hillshade, and optical images. DemDet is able to extract key features from hillshade images, optical images, and DEM, as demonstrated by experimental results on the proposed dataset. In comparison to ResUNet, LandsNet, HRNet, MLP, and SegFormer, DemDet obtains the highest mean accuracy, mIoU, and F1 values, namely 0.95, 0.67, and 0.777. DemDet is therefore capable of autonomously identifying the forest-covered landslides in the Jiuzhaigou earthquake zone. The results of landslide detection mapping reveal that slopes along roads and seismogenic faults are the most crucial areas requiring geohazard prevention.

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

“…In general, the analysis of 2D data is suited to detecting landslides at a large level, e.g., binary detection of significant landslides at coarse resolution. Howev the many challenges of using 2D image data (Table 1), researchers have combine 3D data to understand landslide events better and potentially develop more effec egies to monitor them [24,[46][47][48][49][50]. The 3D geometric analysis can provide deta In general, the analysis of 2D data is suited to detecting landslides at a large granular level, e.g., binary detection of significant landslides at coarse resolution.…”

Modeling the Global Relationship via the Point Cloud Transformer for the Terrain Filtering of Airborne LiDAR Data

“…This is undoubtedly prohibitive for data batches containing tens of thousands of point clouds. The computational complexity of attention after the first I layer is O M 2 , while for the n-th I layer, it is O (M/(nS)) 2 . When directly computed, the complexity is O N 2 × n. Given the high complexity of computing inter-point attention and the significance of the encoder in a model, the E-OA module is employed after I.…”

“…Digital Elevation Models (DEMs) are digital models used to describe the height and shape of a terrain surface and have been widely used in various fields such as surveying, hydrology, meteorology, geological hazards [1,2], soil, engineering construction, and communication [3][4][5]. Airborne LiDAR [6], as one of the main methods for obtaining DEMs [7][8][9], has become irreplaceable in obtaining large-scale and high-precision geographical scene data due to its high efficiency, strong penetration, and other characteristics.…”

“…It leverages the powerful global modeling capabilities of the transformer to learn semantic relationships in large-scale geospatial scenes. (2) We guide the model beyond its local "comfort zone" by introducing global semantics in addition to local relationship modeling. This approach helps establish connections between different local areas, encouraging the model to learn both intra-class and inter-class relationships.…”

Landslide Detection Based on Multi-Direction Phase Gradient Stacking, with Application to Zhouqu, China

“…Similarly, some scholars have used the NASA Uninhabited Aerial Vehicle SAR (UAVSAR) data and the phase gradient stacking method to explore the deformation boundary of monomer landslides [32]. Additionally, some scholars have applied the deep learning method to the phase gradient stacking results for the automatic identification of landslides [33,34].…”