Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

Ahmadi, Salman; Zoej, Mohammad Javad Valadan; Ebadi, Hamid; Moghaddam, Hamid Abrishami; Mohammadzadeh, Ali

doi:10.1016/j.jag.2010.02.001

Cited by 139 publications

(84 citation statements)

References 31 publications

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“…Afterwards, Theng [13] fully automates the system by adding an automatic corner detector that initializes the active contour model. Ahmady et al [14] and Ge et al [15] initialize the contours using regularly distributed circumferences that progressively merge after some repetition. Ok [16] applies the active contours technique specifically to simplify and merge the detected edges.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

et al. 2011

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Abstract:In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

et al. 2011

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“…To date, experts have developed several automatic and semiautomatic procedures to rapidly delineate roof outlines of buildings from high-resolution optical remote sensing images either with a particular purpose of characterizing earthquake risks (Table 1) or with a wider aim (e.g., Ahmadi et al 2010;Ghaffarian and Ghaffarian 2014;Gianinetto 2008;Ouzounis et al 2012;Shorter and Kasparis 2009). However, it remains difficult to apply these technically advanced procedures to irregularly shaped buildings and to identify and eliminate shading, interference, and high gray-tone similarity effects.…”

Section: Estimating Building Footprint Areas From Ge Imagesmentioning

confidence: 99%

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China

Sun

et al. 2016

Nat Hazards

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This paper presents the development of an ''Internet?'' approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth. This approach is a combination of the following interdependent components: (1) extraction of footprint areas of a large number of buildings from high-resolution Google Earth images; (2) estimation of floor numbers of these buildings with an integrated use of high-resolution Google Earth images, Tencent/Baidu Street Views, crowdsourcing data, and associated building-relevant local knowledge; and (3) identification of structural types of these buildings by a combined use of crowdsourcing data and associated buildingrelevant local knowledge. The efficacy of this ''Internet?'' approach was demonstrated through an application in Tangshan, China. Field-based verification indicated that the overall mean absolute percentage error of the proposed ''Internet?'' approach in assessing the total floor area of the addressed buildings was 4.64 %. The verification also showed that the overall consistency between the estimated structural types using the proposed approach and the actual structural types of the buildings with structural type uncertainties could reach 97.54 %, with a kappa coefficient of 0.94. Because of its good accuracy, noteworthy speed, substantial labor savings, negligible cost and distinctive capability in covering large areas in near real time, this ''Internet?'' approach might have promising prospects in actual seismic loss risk reduction challenges.

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“…Nowadays, existing active contour models based on implementation method are classified into two main categories: parametric and geometric models. Parametric snakes are represented explicitly as parameterized contours, and the snake evolution is only performed on a predetermined spline control points (Ahmadi et al, 2010). Geometric models are represented implicitly as the zero-level sets of higher dimensional surfaces, and the updating is performed on the surface function within the entire image domain (Liu and Prinet, 2005).…”

Section: Active Contour Modelsmentioning

confidence: 99%

“…The most important advantage of the proposed model is the less sensitivity to noisy images. Various works in related to this field have executed (Ahmadi et al, 2010;Lie et al, 2006). As mentioned in previous section, edge-based geometric active contour is similar to region-based with difference that the edgebased methods utilize gradient information derived from image to move the contour towards object boundaries.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Automatic Building Map Updating Using Worldview-2 Stereo Images and Existing Outdated Building Map

Gharibi

Arefi

Rastiveis

et al. 2014

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

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ABSTRACT:Updating of existing geo-database, particularly in developing countries, is one of the important and essential issues in geospatial information systems. Numerous change detection methods have been proposed to resolve this issue. In this study, a novel approach is proposed for automatic building change detection and updating from available outdated building map and products of satellite stereo images. The proposed method consists of four steps. In the first step, preliminary building outline for a candidate building is extracted from available old polygon and DSM based on an active contour model. Then, some change detection rules are considered to find out whether or not the building has been changed. If changes are detected, the following third and fourth steps based on a hierarchical approach, run to generate precise changed building outline. The proposed method is tested and evaluated using sample dataset from Tunis City and the obtained results prove the feasibility of this algorithm for automatic building map updating using high resolution stereo satellite images.

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Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

Cited by 139 publications

References 31 publications

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China

Automatic Building Map Updating Using Worldview-2 Stereo Images and Existing Outdated Building Map

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