DEM Generation and Building Detection from Lidar Data
Object reconstruction has attracted great attention from both computer vision and photogrammetry communities, and new technologies are being introduced into this research society. Lidar (Light Detection And Ranging) has become well recognized in the geomatics community since the late 1990s. Compared with traditional photogrammetry, lidar has advantages in measuring surface in terms of accuracy and density, automation, and fast delivery time. There is a large market in geo-data acquisition and object recognition for lidar technology (Baltsavias, 1999). In a general sense, lidar is a companion technology for traditional photogrammetry. The direct product that can be derived from lidar data is the DSM (Digital Surface Model), which depicts the topography of the earth’s surface, including objects above the terrain. Further processing can be carried out to generate DEM (Digital Terrain Model) and object models like buildings, which is very useful information in telecommunication, city planning, flood control, and tourism. Morphology and classification are two commonly used methods in DEM generation and object reconstruction. However, these two methods are either sensitive to errors or of low accuracy. In this paper, a new method is proposed to extract ground points for DEM generation and to detect points belonging to buildings. A new method for boundary regularization is also proposed. The results show that buildings can be detected with high accuracy from lidar data.
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Document Type: Research Article
Publication date: July 1, 2005
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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