An automatic building reconstruction method: a structural approach using high resolution satellite images (original) (raw)
Related papers
2015
Recently, especially within the last two decades, the demand for DSMs (Digital Surface Models) and 3D city models has increased dramatically. This has arisen due to the emergence of new applications beyond construction or analysis and consequently to a focus on accuracy and the cost. This thesis addresses two linked subjects: first improving the quality of the DSM by merging different source DSMs using a Bayesian approach; and second, extracting building footprints using approaches, including Bayesian approaches, and producing 3D models.
Automatic Building Reconstruction from Aerial Images: A Generic Bayesian Framework
2000
A novel system for automatic building reconstruction from multiple aerial images is presented. Compared to previous works, this approach uses a very generic modeling of buildings as polyhedral shapes with no overhang, in which external knowledge is introduced through constraints on primitives. Using planes as base primitives, the algorithm builds up an arrangement of planes from which a 3D graph
Automatic Model Selection for 3d Reconstruction of Buildings from Satellite Imagary
Through the improvements of satellite sensor and matching technology, the derivation of 3D models from space borne stereo data obtained a lot of interest for various applications such as mobile navigation, urban planning, telecommunication, and tourism. The automatic reconstruction of 3D building models from space borne point cloud data is still an active research topic. The challenging problem in this field is the relatively low quality of the Digital Surface Model (DSM) generated by stereo matching of satellite data comparing to airborne LiDAR data. In order to establish an efficient method to achieve high quality models and complete automation from the mentioned DSM, in this paper a new method based on a model-driven strategy is proposed. For improving the results, refined orthorectified panchromatic images are introduced into the process as additional data. The idea of this method is based on ridge line extraction and analysing height values in direction of and perpendicular to the ridgeline direction. After applying pre-processing to the orthorectified data, some feature descriptors are extracted from the DSM, to improve the automatic ridge line detection. Applying RANSAC a line is fitted to each group of ridge points. Finally these ridge lines are refined by matching them or closing gaps. In order to select the type of roof model the heights of point in extension of the ridge line and height differences perpendicular to the ridge line are analysed. After roof model selection, building edge information is extracted from canny edge detection and parameters derived from the roof parts. Then the best model is fitted to extracted façade roofs based on detected type of model. Each roof is modelled independently and final 3D buildings are reconstructed by merging the roof models with the corresponding walls.
A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data
Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06), 2006
Building footprints have been shown to be extremely useful in urban planning, infrastructure development, and roof modeling. Current methods for creating these footprints are often highly manual and rely largely on architectural blueprints or skilled modelers. In this work we will use aerial LIDAR data to generate building footprints automatically. Existing automatic methods have been mostly unsuccessful due to large amounts of noise around building edges. We present a novel Bayesian technique for automatically constructing building footprints from a pre-classified LI-DAR point cloud. Our algorithm first computes a boundederror approximate building footprint using an application of the shortest path algorithm. We then determine the most probable building footprint by maximizing the posterior probability using linear optimization and simulated annealing techniques. We have applied our algorithm to more than 300 buildings in our data set and observe that we obtain accurate building footprints compared to the ground truth. Our algorithm is automatic and can be applied to other man-made shapes such as roads and telecommunication lines with minor modifications.
Large-Scale Building Modeling System Based on Satellite Image
With the increasing demands for 3D building information, an effective method for extraction of 3D building is necessary. From the practical usage point of view, an interactive system for 3D building modeling for a single satellite image based on a semiautomatic method is presented. In order to model a 3D building and minimize user interaction, we utilize geometric features of the satellite image and the geometric relationship among the building, the building shadow, the positions of the sun and the satellite.
Automatic 3D Reconstruction of Buildings Roof Tops in Densely Urbanized Areas
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018
3D reconstruction of the urban environment constitutes a well-studied problem in the field of photogrammetry and computer vision, attracting the growing interest of the scientific community, for many years. Although the current state of the art present very impressive results, there is still room for improvements. The production of reliable and accurate 3D reconstructions is useful for a wide range of applications, such as urban planning, GIS, tax assessment, cadastre, insurance, 3D city modelling, etc. In this paper, a methodology for the automatic 3D reconstruction of buildings roof tops in densely urbanized areas, utilizing dense point clouds data, is proposed. It consists of three (3) main phases, each of which comprises a set of processing steps. In the first phase, the point cloud is simplified and smoothed. Outliers and non-roof elements are detected and removed utilizing shape, position and area criteria. In the second phase, the geometry buildings roof tops is optimized, by detecting and normalizing the edges. In the last phase, the reconstruction of the buildings roof tops is conducted. A progressive process, utilizing a plane fitting algorithm in combination with Screened Poisson Surface Reconstruction is performed. Buildings roof tops surfaces are produced and optimized. A software tool is developed and utilized for the implementation of the proposed methodology. The produced results are assessed and a comparison with another open-source software is conducted. The proposed methodology seems to be effective providing satisfactory results, as it can manage properly the really noisy point clouds of densely urbanized environments.
Performance Assessment of Fully Automatic Three-Dimensional City Model Reconstruction Methods
Three-dimensional urban region representations can be used for detailed urban monitoring, change and damage detection purposes. In order to obtain three-dimensional representation, one of the easiest and cheapest way is to use Digital Surface Models (DSMs) which are generated from very high resolution stereo satellite images using stereovision techniques. Unfortunately after applying the DSM generation process, we cannot directly obtain three-dimensional urban region representation. In the DSM which is generated using only one stereo image pair, generally noise, matching errors, and uncertainty on building wall locations are very high. These undesirable effects prevents a DSM to provide a realistic three-dimensional city representation. Therefore, some automatic techniques should be applied to obtain three-dimensional city models using DSMs as input. In order to solve the existing problems in this field, herein we introduce two automated approaches based on usage of DSMs as input. The first method depends on using of a 3D active shape model for building shape extraction and 3D reconstruction, the second approach is based on an approximation of prismatic models to DSMs. Our experimental results on images and DSMs of Tunis city which are obtained from WorldView-2 satellite indicate possible usage of the proposed algorithms to obtain three-dimensional city representations automatically.
South African Journal of Geomatics
Light detection and ranging (LiDAR) technology has become a standard tool for threedimensional mapping because it offers fast rate of data acquisition with unprecedented level of accuracy. This study presents an approach to accurately extract and model building in threedimensional space from airborne laser scanning data acquired over Universiti Putra Malaysia in 2015. First, the point cloud was classified into ground and non-ground xyz points. The ground points was used to generate digital terrain model (DTM) while digital surface model (DSM) was produced from the entire point cloud. From DSM and DTM, we obtained normalise DSM (nDSM) representing the height of features above the terrain surface. Thereafter, the DSM, DTM, nDSM, laser intensity image and orthophoto were combined as a single data file by layer stacking. After integrating the data, it was segmented into image objects using Object Based Image Analysis (OBIA) and subsequently, the resulting image object classified into four land cover classes: building, road, waterbody and pavement. Assessment of the classification accuracy produced overall accuracy and Kappa coefficient of 94.02% and 0.88 respectively. Then the extracted building footprints from the building class were further processed to generate 3D model. The model provides 3D visual perception of the spatial pattern of the buildings which is useful for simulating disaster scenario for emergency management.