Abstract
Abstract. The extraction of geometric and semantic information from image and range data is one of the main research topics. Between the different geomatics products, 3D city models have shown to be a valid instrument for several applications. As a consequence, the interest for automated solutions able to speed up and reduce the costs for 3D model generation is greatly increased. Image matching techniques can nowadays provide for dense and reliable point clouds, practically comparable to LiDAR ones in terms of accuracy and completeness. In this paper a methodology for the geometric reconstruction of roof outlines (eaves, ridges and pitches) from aerial images is presented. The approach keeps in count the fact the usually photogrammetrically derived point clouds and DSMs are more noisy with respect to LiDAR data. A data driven approach is used in order to keep the maximum flexibility and to achieve satisfying reconstructions with different typologies of buildings. Some tests and examples are reported showing the suitability of photogrammetric DSM for this topic and the performances of the developed algorithm in different operative conditions.
Highlights
The extraction of geometric and semantic information from image and range data is one of the main research topics in the geomatics community
Blunders are usually characterized by chaotic and rough depth variations: this is usually true for the results provided by several matching algorithms
Points provided by image matching algorithms can be randomly noisy
Summary
The extraction of geometric and semantic information from image and range data is one of the main research topics in the geomatics community. The paper presents an automated methodology for the geometric reconstruction of the main roof outlines (eaves, ridges and pitches) from dense point clouds automatically extracted from aerial images. Photogrammetric point clouds and DSM are usually noisier than LiDAR data as they suffers from the radiometric image quality, image overlap, presence of shadows and object texture, as underlined in (Vallet et al, 2011). Many approaches focusing on roof shapes extraction from elevation data have been presented, mainly based on prismatic shapes, point cloud segmentation, feature recognition or DSM simplification (Haala and Kada, 2011). Several commercial software devoted to man-made feature extraction and 3D reconstruction have been developed too These approaches have been originally implemented based on LiDAR as input data, but several problems arise when photogrammetric DSM are used.
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