Research on the Three-Dimensional Information Extraction of Urban Buildings from Single QiuckBird Remote Sensing Image

Wang, Jing Wei; Jiang, Yan; Guo, Zheng

doi:10.4028/www.scientific.net/amm.90-93.3298

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…For challenge (3), existing research on building height estimation based on street view images is very limited. Most scholars (W. Ding et al, 2008;Han, 2011;Yuan & Cheriyadat, 2016) have calculated the height of some objects through normal images based on geometric relationships. For instance, Al-Habashna (2020) and Díaz and Arguello (2016) proposed the algorithms for estimating the heights of buildings using single view images in the street view map.…”

Section: Introductionmentioning

confidence: 99%

Building height calculation for an urban area based on street view images and deep learning

Zhang

et al. 2022

Computer aided Civil Eng

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The building heights of an urban area are useful for space analysis, urban planning, and city management. To this end, a novel method for building height calculation for an urban area is proposed based on street view images and a deep learning model, that is, mask region-based convolutional neural network (Mask R-CNN). First, a spider of street view maps was developed, and an optimization model for observation locations was designed based on a genetic algorithm, by which the street view images of all buildings can be obtained with the minimum number of downloads. Subsequently, a deep learning workflow was designed based on the Mask R-CNN to detect buildings from the panorama images.Finally, an accurate height calculation model considering repeated detection of buildings was developed by mapping between detected buildings and actual buildings. Case studies indicate that the mean error of height calculation is 0.78 m, which achieves high precision for calculating building heights in urban areas, while the average calculation time is 4.57 s per building, which indicates that the proposed method is efficient for the application in urban areas.

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Section: Introductionmentioning

confidence: 99%

Building height calculation for an urban area based on street view images and deep learning

Zhang

et al. 2022

Computer aided Civil Eng

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“…The process requires a lower level of professional knowledge than alternative processes but still achieves good extraction accuracy. Fortunately, Barista is a software package designed for extracting three-dimensional information from high-resolution satellite imagery via visual interpretation [29]. It can directly measure the building height and automatically calculate the building area, and form a 3D building model.…”

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confidence: 99%

Analysis of Three-Dimensional Space Expansion Characteristics in Old Industrial Area Renewal Using GIS and Barista: A Case Study of Tiexi District, Shenyang, China

Liu

et al. 2019

Sustainability

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With rapid urban development in China in the last two decades, 3D characteristics have been the main feature of urban morphology. Nevertheless, the vast majority of urban growth research has only focused on area expansion horizontally, with few studies conducted in a 3D perspective. In this paper, the characteristics of 3D expansion that occurred in Tiexi from 1997 to 2011 were evaluated based on geographic information system (GIS) tools, remote-sensing images, and Barista software. Landscape index, the spatiotemporal distribution of changes in buildings’ renewal modes and variations in city skylines as well as the relationship between number and size of high-rise buildings are the specific phenomena and data utilized to quantify the 3D urban expansion. The results showed that the average height of Tiexi increased by 0.69 m annually, the average urban capacity increased by 490.15 m3 annually, and space congestion degree increased by 0.11% annually. The average annual increase of the building evenness index was 36.43. The renewal area occupied up to 75.38% of the total area. The change of the skyline was more consistent with the east–west direction. The change in the south direction was significant, while in the north direction it was relatively slow. The overall shape of the city was that of a weak pyramid, with the angle of the top of the pyramid gradually becoming larger. The methods proposed in this paper laid a foundation for a wide range of study of 3D urban morphology changes.

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Research on the Three-Dimensional Information Extraction of Urban Buildings from Single QiuckBird Remote Sensing Image

Cited by 2 publications

References 3 publications

Building height calculation for an urban area based on street view images and deep learning

Building height calculation for an urban area based on street view images and deep learning

Analysis of Three-Dimensional Space Expansion Characteristics in Old Industrial Area Renewal Using GIS and Barista: A Case Study of Tiexi District, Shenyang, China

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