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Repeat aerial photography is used to obtain closely spaced measurements of velocity and elevation over a complete transect of Ice Stream tributary B2, including the shear margins, the fast ice of the ice stream and several unusual features, as well as the UpB camp. Persistent features, mainly crevasses, are tracked to provide 1541 values of velocity and 1933 values of elevation. These are used to describe ice flow in the ice stream. Within the ice stream, the dominant velocity gradient is lateral shear. Crevasse patterns are studied in relation to measured velocity gradients. Crevasses intersect one another at acute angles, indicating that their origin is deeper than the depth to which crevasses penetrate. One feature within the ice stream seems to be a raft of stiff ice. Others are crevasse trains. Also, there are spreading ridges, perhaps due to upwelling ice. There is no evidence of large sticky spots within the studied transect, i.e. no steep surface slopes with associated surface stretching just up-glacier and surface compression down-glacier.

Lidar (light detection and ranging) data implicitly contains abundant three‐dimensional spatial information. The segmentation of lidar point clouds is the key procedure for transforming implicit spatial information into explicit spatial information. Common criteria used for point cloud segmentation are proximity and coherence of point distribution. An effective segmentation algorithm may apply various steps or combinations of criteria depending on the application. This paper proposes a four‐step segmentation method for lidar point clouds to deliver incremental segmentation results. Segmentation results of each step can provide the fundamental data for the next step. In the first step, the input point cloud is organised into an octree‐structured voxel space, in which the point neighbourhood is established. In the second step, connected voxels which are not empty are grouped to obtain grouped points based on proximity. The third step is a coplanar point segmentation based on both coherence and proximity, which was performed on each point group obtained in the second step. Finally, neighbouring coplanar point groups are merged into “co‐surface” point groups based on the criteria of plane connection and intersection. This scheme enables an incremental retrieval and analysis of a large lidar data‐set. Experimental results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial lidar data. It is anticipated that the incremental segmentation results will be useful for object modelling using lidar data.

This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths) could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation) when classifying land cover.

mostly implemented in a semi-automatic manner, solving the Building extraction based on pre-established models has been model-image fitting problem based on some high-level inforrecognized as a promising idea for acquiring 3D data for buildmation given by the operator. The spatial data of a building ings from aerial images. This paper proposes a novel building object are determined, when model-image fitting is achieved. extraction method developed from the concept of fitting CSG In contrast to the traditional point-by-point mapping proce-(Constructive Solid Geometry) primitives to aerial images. To dure, model-based building extraction features object-based be practicable, this method adopts a semiautomatic procedata acquisition. Although the idea and benefits of modeldure, carrying out high-level tasks (building detection, model based building extraction have been acknowledged, the workselection, and attribution) interactively by the operator and ing principle is not well established. Therefore, the focus of performing optimal model-image fitting automatically with a this study is to establish a practical theory for model-based least-squares fitting algorithm. Buildings, represented by CSG building extraction. models, can be reconstructed part by part after fitting each Building modeling and model-image fitting are the key parameterized CSG primitive to the edge pixels of aerial issues in model-based building extraction. The issue on buildimages. Reconstructed building parts can then be combined ing modeling is how to establish a set of representative and using CSG Boolean set operators. Consequently, a building is complete building models. This paper reviews some building represented by a CSG tree in which each node links two model schemes known in the field of digital photogrammetry branches of combined parts. This paper demonstrates ten and discusses how CSG modeling is employed in the proposed examples of building extraction from aerial photos taken at a method. The issue in model-image fitting is how to develop a scale of 1:5,000 and scanned at a pixel size of 25 m. All of computer algorithm that is able to determine the pose and the tests were performed in the prototypal system implemented shape parameters of an object model such that the edge lines of in a CAD-based environment cooperated with a number of the wire frame, as projected into the images, are optimally specially designed programs. The process time for each primcoincident with the corresponding edge pixels. It is assumed itive is about 20 seconds and the successful rate of modelthat the image orientations are known and that the pose and image fitting was about 90 percent. Evaluated with some check shape parameters are approximately determined through an points, the fitting accuracy was about 0.3 m horizontally and interactive manual process. To deal with this problem, this 1 m vertically. The test results are encouraging and promote paper proposes a tailored least-squares model-image fitting the theory of model-based building extraction. algori...

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A land-based mobile mapping system (MMS) is flexible and useful for the acquisition of road environment geospatial information. It integrates a set of imaging sensors and a position and orientation system (POS). The positioning quality of such systems is highly dependent on the accuracy of the utilized POS. This limitation is the major drawback due to the elevated cost associated with high-end GPS/INS units, particularly the inertial system. The potential accuracy of the direct sensor orientation depends on the architecture and quality of the GPS/INS integration process as well as the validity of the system calibration (i.e., calibration of the individual sensors as well as the system mounting parameters). In this paper, a novel single-step procedure using integrated sensor orientation with relative orientation constraint for the estimation of the mounting parameters is introduced. A comparative analysis between the proposed single-step and the traditional two-step procedure is carried out. Moreover, the estimated mounting parameters using the different methods are used in a direct geo-referencing procedure to evaluate their performance and the feasibility of the implemented system. Experimental results show that the proposed system using single-step system calibration method can achieve high 3D positioning accuracy.