Building Outline Extraction from Digital Elevation Models Using Marked Point Processes (original) (raw)
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A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a priori knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm.
This work presents a framework to use stochastic geometry for automatic building extraction from different kinds of Digital Elevation Models (DEM). The goal is to extract some vectorial information from a DEM in order to ease precise 3 dimensional reconstruction. Using a spatial point process framework, we model cities as configurations of unknown number of rectangles. An energy is defined, which takes into account both a low level information provided by the altimetry of the scene, and some geometric knowledge on the location of buildings in towns. The estimation is done by minimizing an energy using simulated annealing. We present results on real data provided by IGN (French National Geographic Institute) consisting of laser and optical DEMs.
Automated building extraction: comparison of paradigms and examples
This paper compares the paradigms of LiDAR and aerophotogrammetry in the context of building extraction and briefly discusses two roof building contour extraction methodologies. The assets and drawbacks of both data capturing system have been reported several times. In general, empirical and theoretical studies have confirmed that LiDAR methodologies are more suitable in deriving building heights and in extracting planar roof faces and ridges of the roof, whereas the aerophotogrammetry are more suitable in extracting building roof outlines. The first roof contour extraction methodology is based on a Digital Elevation Model (DEM), which is generated through the regularization of an available LiDAR point cloud. First, in order to detect aboveground objects, the DEM is segmented through a recursive splitting technique, followed by a Bayesian merging technique. The aboveground object polygons are extracted by using vectorization and polygonization techniques. Finally, the building roof contours are identified among all aboveground objects extracted previously, taking into account roof features and a Markov Random Field (MRF) model. The second methodology addresses the geometric refinement of laser-derived 3D roof contours by using high-resolution aerial images and a MRF model. First, 3D roof contours are projected onto the image-space. Then, the projected contours and the straight lines extracted from the image are used to establish an MRF description. The solution of the associate energy function provides groupings of straight lines representing roof building contours, which are topologically reconstructed based on the topology of the projected contours. Examples of applications are provided for both approaches.
An automatic building extraction method: Application to 3D-city modeling
2006
Résumé: In this report, we present an automatic building extraction method from Digital Elevation Models (DEMs). The DEMs are generated using an algorithm based on a maximum-flow formulation using three-view images. First, the building footprints are extracted from the DEMs through an automatic method based on marked point processes: they are represented by an association of rectangles. Then, these rectangular building footprints are regularized by improving both the connection of the neighboring rectangles ...
ISPRS Journal of …, 2008
In this paper, we present an automatic building extraction method from Digital Elevation Models based on an object approach. First, a rough approximation of the building footprints is realized by a method based on marked point processes: the building footprints are modeled by rectangle layouts. Then, these rectangular footprints are regularized by improving the connection between the neighboring rectangles and detecting the roof height discontinuities. The obtained building footprints are structured footprints: each element represents a specific part of an urban structure. Results are finally applied to a 3D-city modeling process.
High-Level Facade Image Interpretation using Marked Point Processes
2016
In this thesis, we address facade image interpretation as one essential ingredient for the generation of high-detailed, semantic meaningful, three-dimensional city-models. Given a single rectified facade image, we detect relevant facade objects such as windows, entrances, and balconies, which yield a description of the image in terms of accurate position and size of these objects. Urban digital three-dimensional reconstruction and documentation is an active area of research with several potential applications, e.g., in the area of digital mapping for navigation, urban planing, emergency management, disaster control or the entertainment industry. A detailed building model which is not just a geometric object enriched with texture, allows for semantic requests as the number of floors or the location of balconies and entrances. Facade image interpretation is one essential step in order to yield such models. In this thesis, we propose the interpretation of facade images by combining evi...
A marked point process for automated building detection from lidar point-clouds
Remote Sensing Letters, 2013
This letter presents a novel algorithm for automated building detection from light detection and ranging (lidar) point-clouds. The algorithm takes advantage of a marked point process to model the locations of buildings and their geometries. A Bayesian paradigm is used to obtain a posterior distribution for the marked point process. A Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm is implemented for simulating the posterior distribution. Finally, the maximum a posteriori (MAP) scheme is used to obtain an optimal building detection. The results obtained on a set of lidar point-clouds demonstrate the efficiency of the proposed algorithm in automated detection of buildings in complex residential areas.
Image Processing, …, 2006
We present an automatic 3D city model of dense urban areas from high resolution satellite data. The proposed method is developed using a structural approach : we construct complex buildings by merging simple parametric models with rectangular ground footprint. To do so, an automatic building extraction method based on marked point processes is used to provide rectangular building footprints. A collection of 3D parametric models is defined in order to be fixed onto these building footprints. A Bayesian framework is then used : we search for the best configuration of models with respect to both a prior knowledge of models and their interactions, and a likelihood which fits the models to the Digital Elavation Model. A simulated annealing scheme allows to find the configuration which maximizes the posterior density of the Bayesian expression.
AUTOMATIC BUILDING EXTRACTION FROM LIDAR DATA COVERING COMPLEX URBAN SCENES
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 25-32, 2014
This paper presents a new method for segmentation of LIDAR point cloud data for automatic building extraction. Using the ground height from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points by applying the following two approaches: If a plane fitted at a point and its neighbourhood is perpendicular to a fictitious horizontal plane, then this point is designated as a wall point. When LIDAR points are projected on a dense grid, points within a narrow area close to an imaginary vertical line on the wall should fall into the same grid cell. If three or more points fall into the same cell, then the intermediate points are removed as wall points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. One or more clusters are initialised based on the maximum height of the points and then each cluster is extended by applying height and neighbourhood constraints. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. If the estimated height of a point is similar to its LIDAR generated height, or if its normal distance to a plane is within a predefined limit, then the point is added to the plane. Once all the planar segments are extracted, the common points between the neghbouring planes are assigned to the appropriate planes based on the plane intersection line, locality and the angle between the normal at a common point and the corresponding plane. A rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on long line segments. Experimental results on ISPRS benchmark data sets show that the proposed method offers higher building detection and roof plane extraction rates than many existing methods, especially in complex urban scenes.
Automatic processing of Terrestrial Laser Scanning data of building façades
Automation in Construction, 2012
Feature extraction on façades from unstructured point clouds is a challenging work, especially in the presence of noise. Point cloud segmentation is one of the most important steps in this context. In this paper, a new approach for automatic processing of façade laser scanner data is introduced. Scanner orientation is partially known through the inclination sensors of the laser scanner used. Knowing these values allows us to reduce the point cloud data into a profile distribution function. After orientation, this distribution is a series of peaks and valleys suitable for segmentation. Each segmented layer is afterwards processed to find the façade contours. The results obtained prove that the approach may be successfully employed in building segmentation and extraction of planar features. Moreover, the accuracy of contours is very dependent on the resolution of the scan data.