A Shadow-Overlapping Algorithm for Estimating Building Heights From VHR Satellite Images

Kadhim, Nada; Mourshed, Monjur

doi:10.1109/lgrs.2017.2762424

Cited by 38 publications

(24 citation statements)

References 17 publications

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“…It covers approximately 225 km 2 of the urban area and includes the districts Cẩm Lệ, Hải Châu, Liên Chiểu, Ngũ Hành Sơn, Sơn Trà and Thanh Khê. Since its classification as a Class I city in 1997, Da Nang underwent rapid socio-economic and environmental changes (JICA, 2010). As a consequence, large shrubland and forest areas were logged down for urban extension areas in many parts of the city (Linh et al, 2009).…”

Section: Study Area and Datamentioning

confidence: 99%

DSM-based identification of changes in highly dynamic urban agglomerations

Warth

Braun

Bödinger

et al. 2019

European Journal of Remote Sensing

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Rapidly urbanizing areas are challenged by a lack of information on urban growth in many parts of the earth. As the speed of building construction often exceeds traditional surveying methods, remote sensing can serve as a valuable input for the monitoring of urbanization processes. This study investigates changes in DaNang, Vietnam, between 2015 and 2017 identified based on photogrammetric analysis of surface elevations retrieved from Pléiades very high-resolution imagery. In contrast to traditional post-classification change detection approaches, we propose a time-efficient method solely based on digital surface model differencing to identify newly constructed buildings as well as demolitions. It is therefore easy to apply and suitable for the continuation of outdated base data available to local authorities. High importance is addressed to the vertical matching of both surface models to avoid misdetections. After differencing these surface elevations, thresholds based on field measurements are applied to identify areas of change. A total of 10,800 changes were detected between 2015 and 2017, of which 8,531 were to newly constructed buildings. The study proves that changes in rapidly urbanizing agglomerations can be reliably identified by a simple and transparent approach by using elevation changes and expert-based knowledge on floor numbers and building heights. ARTICLE HISTORY

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Section: Study Area and Datamentioning

confidence: 99%

DSM-based identification of changes in highly dynamic urban agglomerations

Warth

Braun

Bödinger

et al. 2019

European Journal of Remote Sensing

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“…Shadow regions are distinguished from non-shadow areas based on the characteristics of VHR satellite ortho-images using the bands (green (G), red (R), and near-infrared band (NIR)). Similar to our previous work (Kadhim , Mourshed, 2018), we detected an extracted shadow regions of the existing buildings in the image space. Therefore, in our new method, we produced a binary image by applying Normalized Differential Vegetation Index (NDVI) to detect vegetated areas and the automatic histogram thresholding based on Otsu's method (Otsu, 1979) to compute a binary vegetation mask.…”

Section: Methodsmentioning

confidence: 99%

“…For example, (Redweik et al, 2013) built a shadow algorithm to calculate shadow maps for the solar potential assessment of all surfaces (roofs and facades) of buildings within an urban landscape. (Kadhim , Mourshed, 2018) developed a new algorithm to estimate the height of the buildings based on the extracted shadow areas from VHR multispectral images. (Yan et al, 2010) examined the form shadow and cast shadow changes on the surfaces of architectural heritage buildings to retrieve their original colours for recording and documenting the chromatic information.…”

Section: Previous Studiesmentioning

confidence: 99%

“…The displacement between the coordinates of the same corner point in both the reference and test images belongs to the building edges are not utterly met each other at that corner due to the process of morphological dilation and erosion that conducted on the stage of the shadow detection in which may affect on losing a part of the edge. We assessed the algorithm performance of the proposed approach by employing the well-known three metrics (Precision,Recall,F1) to evaluate the pixel-based performance of the proposed approach as applied in our previous work (Kadhim , Mourshed, 2018)…”

Section: The Outputs Of the Developed Algorithmmentioning

confidence: 99%

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Building Assessment Using Shadow Analysis for the Architectural Documentation

Kadhim

2019

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

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<p><strong>Abstract.</strong> One essential approach in preserving architectural heritage is the documentation of building geometries and their sites are thoroughly photographed of existing buildings. For instance, an automated detection of the locations and the extraction of geometry information of the buildings are intrinsic attributes of the architectural design of building interiors and exteriors in cultural heritage documentation and preservation. In this study, contributions of remotely sensed images to detect the sites and shapes of the buildings based on shadow analysis were evaluated. The aim of this paper is to develop an approach to assist in recording and documenting the external geometry of historic buildings and their real location supplying much ease and time-saving in protection projects of cultural heritage. A distinctive merit of the proposed approach is its ability to document the spatial distribution of the detecting buildings by extracting the graphic coordinates of their corners without any additional data. Our experiments demonstrate the efficiency of the proposed approach on two cases, including the architectural documentation of external edges, shape, size, site, orientation, and coordinates. Thus, our new approach offers a tool to document architectural and cultural heritage which can help in capturing and recording all the relevant data and information, both in visual and written format.</p>

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“…The story areas can be calculated by the polygons of building footprints. Kadhim and Mourshed proposed a shadow-overlapping algorithm for estimating building heights from VHR satellite images [12], by using graph theory and morphological fuzzy processing techniques. Note that imagery between 0.3-0.8 m/pxl is considered VRH satellite imagery in the above research.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning

et al. 2020

Applied Sciences

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Being the necessary data of the city-scale seismic damage simulations, structural types of buildings of a city need to be collected. To this end, a prediction method of structural types of buildings based on machine learning (ML) is proposed herein. Specifically, using the training data of 230,683 buildings in Tangshan city, China, a supervised ML solution based on a decision forest model was designed for the prediction. The scale sensitivity and regional applicability of the designed solution are discussed, respectively, and the results show that the supervised ML solution can maintain high accuracy for different scales; however, it is only suitable for cities similar to the sample city. For wide applicability for various cities, a semi-supervised ML solution was designed based on sampling investigation and self-training procedures. The downtowns of Daxing and Tongzhou districts in Beijing were selected as a case study for the designed semi-supervised ML solution. The overall prediction accuracies of structural types for Daxing and Tongzhou downtowns can reach 94.8% and 99.5%, respectively, which are acceptable for seismic damage simulations. Based on the predicted results, the distributions of seismic damage in Daxing and Tongzhou downtown were output. This study provides a smart and efficient method for obtaining structural types for a city-scale seismic damage simulation.

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A Shadow-Overlapping Algorithm for Estimating Building Heights From VHR Satellite Images

Cited by 38 publications

References 17 publications

DSM-based identification of changes in highly dynamic urban agglomerations

DSM-based identification of changes in highly dynamic urban agglomerations

Building Assessment Using Shadow Analysis for the Architectural Documentation

Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning

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