Hangbin Wu

Landslide Investigation with Remote Sensing and Sensor Network: From Susceptibility Mapping and Scaled-down Simulation towards in situ Sensor Network Design

This paper presents an integrated approach to landslide research based on remote sensing and sensor networks. This approach is composed of three important parts: (i) landslide susceptibility mapping using remote-sensing techniques for susceptible determination of landslide spots; (ii) scaled-down landslide simulation experiments for validation of sensor network for landslide monitoring, and (iii) in situ sensor network deployment for intensified landslide monitoring. The study site is the Taziping landslide located in Hongkou Town (Sichuan, China). The landslide features generated by landslides triggered by the 2008 Wenchuan Earthquake were first extracted by means of object-oriented methods from the remote-sensing images before and after the landslides events. On the basis of correlations derived between spatial distribution of landslides and control factors, the landslide susceptibility mapping was carried out using the Artificial Neural Network (ANN) technique. Then the Taziping landslide, located in the above mentioned study area, was taken as an example to design and implement a scaled-down landslide simulation platform in Tongji University (Shanghai, China). The landslide monitoring sensors were carefully investigated and deployed for rainfall induced landslide simulation experiments. Finally, outcomes from the simulation experiments were adopted and employed to design the future in situ sensor network in Taziping landslide site where the sensor deployment is being implemented.

Automatic Buildings Extraction From LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex

Researchers have extensively applied Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) for segmentation. These networks are neural oscillator networks based on biological frameworks, in which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood, as well as a connection to a global inhibitor. In this paper, we develop a modified LEGION segmentation to extract buildings from high-quality digital surface models (DSMs). The extraction is implemented without assumptions on the underlying structures in the DSM data and without prior knowledge of the number of regions. For complex information hidden in the generated DSM of an urban area, grey level co-occurrence matrix homogeneity is used to measure DSM height texture. We then use this homogeneity to distinguish buildings from trees and identify major oscillator blocks in target buildings, instead of using lateral potential. To segment pixels into different groups, we calculate the weight of the global inhibitor (Wz) from DSM complexity. Building boundaries are traced and regularised after extraction from the segmented DSM. A least squares solution with perpendicular constraints for determining regularised rectilinear building boundaries is proposed, and arc line fitting is performed. This paper presents the concept, algorithms, and procedures of the proposed approach. Experimental results on the Vaihingen region studied in the ISPRS test project are also discussed.

Feature-constrained registration of building point clouds acquired by terrestrial and airborne laser scanners

Abstract Point-cloud registration is usually accomplished on the basis of several corresponding features to compute the parameters of the transformation model. However, common point features are difficult to select because airborne laser scanner (ALS) and terrestrial laser scanner (TLS) point clouds of the same object have be aligned due to the different sensing positions and sampling modes. Taking building profile features as objects, a registration method based on feature constraints is proposed here. The standard six-parameter rigid-body transformation adopted for alignment of laser scans is replaced by a two-step transformation: horizontal registration based on a two-dimensional similarity transformation and vertical registration based on a simple vertical shift. First, the feature-line and feature-plane equation parameters are obtained from both the airborne and terrestrial point clouds. Second, the plane transformation parameters are computed after projecting the extracted features onto a horizontal reference plane. Finally, the elevation transformation parameter is calculated by comparing the heights of flat features. The ALS and TLS datasets of two buildings (Shanghai Pudong International Conference Center and Shanghai Ocean Aquarium, China) were used to evaluate the robustness and accuracy. The results show that the proposed feature-constrained method works well for registration between two datasets. Five checkpoints and one overlap zone for the Pudong International Conference Center were selected to evaluate the accuracy and resulted in accuracies of 0.15 to 0.5 m in the horizontal direction and 0.20 m in the vertical direction.

Analysis of spatial sensor network observations during landslide simulation experiments

A spatial sensor network was tested during five experiments on a landslide simulation platform. Here, a landslide was triggered by means of simulated rainfall. The sensor network currently incorporates in situ sensors and two stereo imaging systems. In future, these sensors will be installed on a real-scene slopes in Sichuan Province (South-West China). The paper focuses on the results of two latest landslide simulation experiments. While one experiment ended with a partial failure, the second one showed a complete slope collapse. In the first part of the study, the full data series are investigated to perform correlations and common pattern analysis, as well as to link them to the physical processes. In the second part, four subsets of sensors located in neighbouring positions are analysed. Although the small scale of the simulated experiment probably influenced the results, these experiments allowed ascertaining which sensors could be more suitable to be deployed on the real-scene landslide sites.

Model test study on monitoring dynamic process of slope failure through spatial sensor network

Landslides represent a major type of natural hazards worldwide. For development of risk mitigation capabilities, an effective system for monitoring dynamic process of slope failure, capable of gathering spatially distributed information before, during and after a landslide occurrence at real-time manner is essential. A spatial sensor network (SSN), which integrates the real-time communication infrastructure and observations from in situ sensors and remote sensing platforms, offers an efficient and effective approach for such purpose. In this paper, a SSN-based landslide monitoring system was designed and evaluated through a model test study conducted at Tongji University, China. This system, MUNOLD (MUlti-Sensor Network for Observing Landslide Disaster), has been designed as a comprehensive monitoring framework, including sensor observations, multi-channel wireless communication, remote data storage, visualization, data processing and data analysis. In this model test study, initial experimentation demonstrated the capabilities of the MUNOLD system for collecting real-time information about the dynamic process and propagation of slope failure. Innovatively, generated from the high-speed stereo images, the sequential surface deformation vector field can be created and may exhibit the dynamic process during the extremely critical and short period of the slope failure. After this model test study, the MUNOLD system is going to be further improved and extended in a landslide prone region in Sichuan Province, China.

Detection of High-Speed Railway Subsidence and Geometry Irregularity Using Terrestrial Laser Scanning

Subsidence and geometry deformation monitoring are essential for safe transportation on a high-speed railway. Terrestrial laser scanning (TLS) is able to collect dense three-dimensional point data from the survey scene and achieve highly accurate measurements; therefore, it is considered to be one of the most promising surveying techniques for railway track geometry deformation monitoring. This paper proposes a new approach that uses TLS to detect subsidence and irregularities in a track by fitting boundaries of the cross section of the track. In addition, for a section of local railway, an outdoor experiment was performed to ascertain the feasibility and accuracy of this method. The deformations detected with TLS were compared with the field measurements gathered with other methods such as those from a track inspection car. The results indicate that the subsidence difference between TLS and precise leveling is 2–3 mm, and the difference in the geometric parameters of the tracks is 1–2 mm. Finally, the possible causes of error involved with TLS are discussed.

Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring

Early detection and early warning are of great importance in giant landslide monitoring because of the unexpectedness and concealed nature of large-scale landslides. In China, the western mountainous areas are prone to landslides and feature many giant complex landslides, especially following the Wenchuan Earthquake in 2008. This work concentrates on a new technique, known as the “hybrid-SAR technique”, that combines both phase-based and amplitude-based methods to detect and monitor large-scale landslides in Li County, Sichuan Province, southwestern China. This work aims to develop a robust methodological approach to promptly identify diverse landslides with different deformation magnitudes, sliding modes and slope geometries, even when the available satellite data are limited. The phase-based and amplitude-based techniques are used to obtain the landslide displacements from six TerraSAR-X Stripmap descending scenes acquired from November 2014 to March 2015. Furthermore, the application circumstances and influence factors of hybrid-SAR are evaluated according to four aspects: (1) quality of terrain visibility to the radar sensor; (2) landslide deformation magnitude and different sliding mode; (3) impact of dense vegetation cover; and (4) sliding direction sensitivity. The results achieved from hybrid-SAR are consistent with in situ measurements. This new hybrid-SAR technique for complex giant landslide research successfully identified representative movement areas, e.g., an extremely slow earthflow and a creeping region with a displacement rate of 1 cm per month and a typical rotational slide with a displacement rate of 2–3 cm per month downwards and towards the riverbank. Hybrid-SAR allows for a comprehensive and preliminary identification of areas with significant movement and provides reliable data support for the forecasting and monitoring of landslides.

Architecture planning and geo-disasters assessment mapping of landslide by using airborne lidar data and UAV images

After the operation of GPS/IMU direct geo-referencing, segmentation, filtering, classification of scattered point data and aerial triangulation on airborne LiDAR(Light Detection and Ranging) data, the accurate and high-resolution DEM of the study area in the west part of Zengcheng city, Guangdong, China was constructed. In addition, unmanned aerial vehicle (UAV) images were used for ground objects identification. Landslides occur frequently in summer in the city because of heavy rainfall. The LiDAR data (point cloud) and the mosaic images were then combined to produce the suitability distribution maps by considering Several factors, such as slope gradient, slope aspect, on-the-spot investigation data etc The maps can then be used to analyze the potential risk of landslides and assess the risk level around some buildings. The experiment results show that the method based on LiDAR data and UAV images can rapidly and accurately survey the terrain of the study area and also provides useful information for architectural design.

Water feature extraction from aerial-image fused with airborne LIDAR data

An innovative method to extract water feature from aerial-image is introduced in this paper. This approach extracts water feature from coarse to fine considering laser spectral bands of current existing airborne LIDAR systems and the spectral characteristic of these bands. Quad-edge based incremental inserting algorithm is used to construct the TIN (Triangulation Irregular Network) from LIDAR points. According to the triangulate features of different objects, area-analysis is performed to extract water triangles from TIN. Water triangles depict the water location of aerial-image. Then buffering is performed to extend the area of water triangle and to uptake the whole water-related points cloud data. Raster calculation is used here to obtain the rough water feature. Then, Mean-Shift algorithm is used to reclassify the rough water feature and to obtain the precise water. Finally, the feasibility of the approach is verified using comparison between two ordinary methods and the approach proposed in this paper.

Extraction of Urban 3D Features from Lidar Data Fused with Aerial Images Using an Improved Mean Shift Algorithm

Abstract An approach is proposed for the extraction of the urban three-dimensional features efficiently and accurately. In this method, firstly, both the LIDAR data and the aerial images are respectively pre-processed and matched using the affine transformation model . In order to exploit the spectral data and classify the LIDAR data with high accuracy, a data extraction procedure is employed which extracts the converted pixel values of the aerial image to LIDAR data. Then, an improved Mean Shift algorithm is employed to classify the LIDAR data fused with reflected intensity and spectrum attribute into groups by kinds of feature, such as buildings, vegetation, water etc. The classification accuracy is evaluated by space accuracy and confusion matrix evaluation. Finally, the 3D models of interested regions are quickly constructed based on the classified points and the aerial-image by SketchUp. Using this method, the 3D models of urban objects could be easily extracted and constructed.