Feature Evaluation for Building Facade Images – An Empirical Study

Min-hua, Yang; Förstner, Wolfgang; Chai, Douglas

doi:10.5194/isprsarchives-xxxix-b3-513-2012

Cited by 6 publications

(3 citation statements)

References 30 publications

(38 reference statements)

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“…Furthermore, other relevant façade features could be added such as those proposed by Yang et al (2012) based on more sophisticated statistical information about the color space, the geometry and the texture. Finally, various camera configurations and positions (oblique VS vertical) can be tested so as to find the most relevant configurations for such façade analysis.…”

Section: Resultsmentioning

confidence: 99%

Detecting blind building façades from highly overlapping wide angle aerial imagery

Burochin¹,

Vallet²,

Brédif³

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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This paper deals with the identification of blind building façades, i.e. façades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes. We assume that we have at our disposal an aerial survey with a relatively high stereo overlap alongtrack and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each façade the best available resolution texture seeing the whole façade. We then parse all 3D façades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification. Despite the relatively low resolution of the images, we reach a classification accuracy of around 85% on decimeter resolution imagery with 60 Â 40% stereo overlap. On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on façade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections. We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for facade blindness characterisation.

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Section: Resultsmentioning

confidence: 99%

Detecting blind building façades from highly overlapping wide angle aerial imagery

Burochin¹,

Vallet²,

Brédif³

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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“…In the building structure survey of the disaster-bearing body investigation, the size and location of openings in walls are the key factors in understanding the building structure and carrying out damage assessment of disaster-bearing bodies, in which case, window and door elements are one of the most distinctive and numerous elements of the building façade. Compared with traditional extraction methods such as contour-based methods (Haugeard et al, 2009;Lee & Nevatia, 2004;Recky & Leberl, 2010), intensity-based methods (Čech & Šára, 2009), and machine learning-based methods (Jampani et al, 2015;Reznik & Mayer, 2008;Yang et al, 2012), extracting doors and windows with UAV data and deep learning based methods would greatly reduce the difficulty of survey work and improve efficiency. First, rather than conducting large amounts of on-the-spot investigation, unmanned aerial vehicles (UAV), aircraft, satellites, and other equipment are used to scan a specific area and obtain the corresponding data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Mask R-Cnn: Extraction of Door and Window Instances on Village Building Façade Images

Zhong,

He,

Lin

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The development of computer vision technology provides new ideas and tools for identifying and extracting basic structures of housing buildings. Considering that the original Mask R-CNN ignores the spatial association and relationship of door and window elements, an advanced deep learning model based on Mask R-CNN network is proposed in this paper to detect and segment the door and window structure from the façade images. The improved network architectures integrate the attention mechanism with the original network, containing an improved Coordinate Attention(CA) module and a relationship module-based head network. The experimental results show that the Average Precision(AP) value of the backbone combined with the improved CA module is increased by 0.7% and 0.7% on regression and segmentation tasks respectively, compared with the original Mask R-CNN network. In the head network based on the relationship module, the calculation strategy of the relational module proposed in this paper increases the AP values of detection and segmentation from 76.7% and 77.7% to 80.6% and 80.0%, respectively.

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“…The image quality of the facade plots was tested by edge analysis and proved to be as good as the original images. In (Yang, et al 2012) the influence of various object features on the classification of building facades is investigated. The classification is based on images only.…”

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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Feature Evaluation for Building Facade Images – An Empirical Study

Cited by 6 publications

References 30 publications

Detecting blind building façades from highly overlapping wide angle aerial imagery

Detecting blind building façades from highly overlapping wide angle aerial imagery

An Improved Mask R-Cnn: Extraction of Door and Window Instances on Village Building Façade Images

Automated mapping of building facades by machine learning

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