Building Façade Separation in Vertical Aerial Images

Meixner, Philipp; Wendel, Andreas; Bischof, Horst; Leberl, Franz

doi:10.5194/isprsannals-i-3-239-2012

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…For LOD2, building walls are added from every edge of the roof towards the corresponding points on the ground. In this work, we assume the availability of results for façade analysis, that is, a decomposition of the façade polygons into classes "wall", "door", and "window", which is an output of various recently published methods [1][2][3] [10]. Our goal is to represent these classes and their animations in VBS2.…”

Section: Preliminaries On Urban Terrain Modeling From Sensor Datamentioning

confidence: 99%

Automatic representation of urban terrain models for simulations on the example of VBS2

Bulatov

Häufel

Solbrig

et al. 2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

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Virtual simulations have been on the rise together with the fast progress of rendering engines and graphics hardware. Especially in military applications, offensive actions in modern peace-keeping missions have to be quick, firm and precise, especially under the conditions of asymmetric warfare, non-cooperative urban terrain and rapidly developing situations. Going through the mission in simulation can prepare the minds of soldiers and leaders, increase self-confidence and tactical awareness, and finally save lives. This work is dedicated to illustrate the potential and limitations of integration of semantic urban terrain models into a simulation. Our system of choice is Virtual Battle Space 2, a simulation system created by Bohemia Interactive System. The topographic object types that we are able to export into this simulation engine are either results of the sensor data evaluation (building, trees, grass, and ground), which is done fully-automatically, or entities obtained from publicly available sources (streets and water-areas), which can be converted into the system-proper format with a few mouse clicks. The focus of this work lies in integrating of information about building façades into the simulation. We are inspired by state-of the art methods that allow for automatic extraction of doors and windows in laser point clouds captured from building walls and thus increase the level of details of building models. As a consequence, it is important to simulate these animation able entities. Doing so, we are able to make accessible some of the buildings in the simulation

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Section: Preliminaries On Urban Terrain Modeling From Sensor Datamentioning

confidence: 99%

Automatic representation of urban terrain models for simulations on the example of VBS2

Bulatov

Häufel

Solbrig

et al. 2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

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“…Five factors, including line match, line direction, correlation coefficient, sift match and building edge ratios, were employed to confirm the locations of the vertical faç ades in 3D space based on the 3D point cloud data from stereo matching. Meixner and Leberl [11] detected a faç ade mapped in 3D object space based on a 3D point cloud using vertical aerial photography (greater than 20°) instead of utilizing various auxiliary data. Zebedin et al [12] adopted an image optimization-based method to ascertain the positions of faç ades in Digital Surface Model, which was employed to initialize the hypotheses and describe the 3D information of the faç ades.…”

Section: Introductionmentioning

confidence: 99%

Building Façade Recognition Using Oblique Aerial Images

et al. 2015

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This study proposes a method to recognize faç ades from large-scale urban scenes based on multi-level image features utilizing a recently developed oblique aerial photogrammetry technique. The method involves the use of multi-level image features, a bottom-up feature extraction procedure to produce regions of interest through monoscopic analysis, and then a coarse-to-fine feature matching strategy to characterise and match the regions in a stereoscopic model. Feature extraction from typical urban Manhattan scenes is based on line segments. Windows are re-organised based on the spatial constraints of line segments and the homogeneous structure of the spectrum. Façades as regions of interest are successfully constructed with a remarkable single edge and evidence from windows to get rid of occlusion. Feature matching is hierarchically performed beginning from distinctive facades and regularly distributed windows to the sub-pixel point primitives. The proposed strategy can effectively solve ambiguity and multi-solution problems in the complex urban scene matching process, particularly repetitive and poor-texture façades in oblique view.

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“…It revealed accuracies of 76% ("vegetation"), 68% ("window"), and 60% ("building") for the three important classes. In (Meixner, et al 2012) vertical aerial images are used to detect border lines between facades automatically. Their approach uses height profiles and repetitive patterns which are derived from large-format imagery.…”

Section: Introductionmentioning

confidence: 99%

Automated mapping of building facades by machine learning

Höhle

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds of buildings. The planes of the buildings are automatically detected. The derived planes are supplemented with a regular grid of points for which the colour values are found in the images. For each grid point of the façade additional attributes are derived from image and object data. This 'intelligent' point cloud is analysed by a decision tree, which is derived from a small training set. The derived decision tree is then used to classify the complete point cloud. To each point of the regular façade grid a class is assigned and a façade plan is mapped by a colour palette representing the different objects. Some image processing methods are applied to improve the appearance of the interpreted façade plot and to extract additional information. The proposed method is tested on facades of a church. Accuracy measures were derived from 140 independent checkpoints, which were randomly selected. When selecting four classes ("window", "stone work", "painted wall", and "vegetation") the overall accuracy is assessed with 80% (95% Confidence Interval: 71% -88%). The user accuracy of class "stonework" was assessed with 90% (95% CI: 80%-97%). The proposed methodology has a high potential for automation and fast processing.

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Building Façade Separation in Vertical Aerial Images

Cited by 4 publications

References 6 publications

Automatic representation of urban terrain models for simulations on the example of VBS2

Automatic representation of urban terrain models for simulations on the example of VBS2

Building Façade Recognition Using Oblique Aerial Images

Automated mapping of building facades by machine learning

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