Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov

This paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified.

Building roof contour extraction from LiDAR data

This paper proposes a method for the automatic extraction of building roof contours from a LiDAR-derived digital surface model (DSM). The method is based on two steps. First, to detect aboveground objects (buildings, trees, etc.), the DSM is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. Preliminary results have shown that the proposed methodology works properly.

Simulated annealing for building roof contours identification from lidar data

This paper proposes a method by simulated annealing for building roof contours identification from LiDAR-derived digital elevation model. Our method is based on the concept of first extracting aboveground objects and then identifying those objects that are building roof contours. First, to detect aboveground objects (buildings, trees, etc.), the digital elevation model is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing algorithm. Experiments carried out with laser scanning digital elevation model showed that the methodology works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives.

Morfometria de compartimentos hidrológicos e qualidade hídrica do município de Batatais-SP

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Cenários da expansão urbana e da legislação e os reflexos na cobertura vegetal arbórea e arbustiva na cidade de Americana-SP

Pós-graduação em Geografia - IGCE