Cited by Projections of future spatiotemporal urban 3D expansion in China under shared socioeconomic pathways

Mapping of individual building heights reveals the large gap of urban-rural living spaces in the contiguous US DOI

Yangzi Che, Xuecao Li,

Xiaoping Liu

et al.

The Innovation Geoscience, Journal Year: 2024, Volume and Issue: 2(2), P. 100069 - 100069

Published: Jan. 1, 2024

<p>Living spaces are a crucial component of communities and social interactions, whereas the vertical structure buildings in these spaces, particularly at large-scale, has received limited attention yet. Here, we produced detailed height map each building conterminous United States (US) circa 2020. Leveraging multi-source satellite observations footprint data, our study aimed to shed light on spatial variations heights their implications measure inequality living spaces. Our results revealed significant variation heights, with downtown areas exhibiting an average 12.4m, more than double suburban 5.4m. Moreover, highlighted urban-rural gap urban regions offering compared rural areas. This contributes growing body knowledge planning lays foundation for future investigations improving conditions fostering sustainable communities.</p>

Language: Английский

Citations

Urban building height extraction accommodating various terrain scenes using ICESat-2/ATLAS data DOI

Xiang Huang, Feng Cheng, Yinli Bao

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 130, P. 103870 - 103870

Published: May 15, 2024

Although the photon point cloud data acquired from ICESat-2/ATLAS can be efficiently employed in urban building height extraction, its universal applicability undulating terrain scenarios is constrained, and there are noticeable issues of false positives negatives. This research establishes a terrain-adaptive methodological framework based on to extract high-precision, high-density across varied topographical conditions. First, elevation buffer utilized coarse denoise cloud, involving removal majority noise photons scene, thereby enhancing efficiency subsequent algorithms. Second, signal extracted remaining original using Adaptive Method Based Single-Photon Spatial Distribution (SPSD-AM). approach demonstrates high universality various scenes, while simultaneously ensuring stable accuracy extraction. Subsequently, ground fit curve Differences Urban Signal Photons (USPSD-AM), which addresses challenge potential mixing complex scenarios. A precise then photons. In order mitigate such as negatives, post-processing steps, including completion denoising photons, implemented. Finally, adopted accurate parameters. The precision verification results show that heights considerably consistent with reference heights. mean RMSE MAE 0.273 m 0.202 for flat terrains 1.168 0.759 terrains, respectively. proposed method superior diverse scenarios, providing robust theoretical foundation large-scale retrieval efforts.

Language: Английский

Citations

Refining urban morphology: An explainable machine learning method for estimating footprint-level building height DOI

Yang Chen, Wenjie Sun, Ling Yang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 112, P. 105635 - 105635

Published: July 1, 2024

Language: Английский

Citations

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

Zhen Xu, Furong Zhang, Yingying Wu

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2022, Volume and Issue: 38(7), P. 892 - 906

Published: Oct. 10, 2022

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

Language: Английский

Citations

Projections of future spatiotemporal urban 3D expansion in China under shared socioeconomic pathways DOI

Kechao Wang, Tingting He, Wu Xiao

et al.

Landscape and Urban Planning, Journal Year: 2024, Volume and Issue: 247, P. 105043 - 105043

Published: March 11, 2024

Language: Английский

Citations