Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

Zięba-Kulawik, Karolina; Skoczylas, Konrad; Mustafà, Ahmed; Wężyk, Piotr; Gerber, Philippe; Teller, Jacques; Omrani, Hichem

doi:10.3390/rs12213668

Cited by 6 publications

(4 citation statements)

References 46 publications

(51 reference statements)

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“…In general, it is a digital representation of urban areas describing the geometry, structure, and covering data of buildings, infrastructure, vegetation, terrain, and various morphological elements [191]. The research purposes range from noise mapping [20] and urban spatiotemporal change detection [136] to energy applications [137]. They fulfilled more complex spatial analytical tasks than those for classification purposes only.…”

Section: D City Dtmentioning

confidence: 99%

Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation

Ying

Koeva

Kuffer

et al. 2022

IJGI

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Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general context of digital twin (DT) creation are not sufficiently explored. Therefore, this review aims to identify appropriate urban 3D modelling method(s) for city DT, which can be used for 3D property valuation (3DPV) in the future (both short-term and long-term). We focused on 3D modelling studies investigating buildings and urban elements directly linked with residential properties. In total, 180 peer-reviewed journal papers were selected between 2016 and 2020 with a narrative review approach. Analytical criteria for 3D modelling methods were explicitly defined and covered four aspects: metadata, technical characteristics, users’ requirements, and ethical considerations. From this, we derived short-term and long-term prospects for 3DPV. The results provide references for integrating 3D modelling and DT in property valuation and call for interdisciplinary collaboration including researchers and stakeholders in the real estate sector, such as real estate companies, house buyers and local governments.

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Section: D City Dtmentioning

confidence: 99%

Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation

Ying

Koeva

Kuffer

et al. 2022

IJGI

View full text Add to dashboard Cite

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“…TP is the number of samples whose real tags belong to positive class and whose predicted value is also positive class, and FP is the number of samples whose real tags belong to negative class but whose predicted value is positive class. The mathematical definition of recall is shown in Equation (13). As shown in Figure 14, the top of the figure is the main view of the point cloud of the whole FMMF in the Y direction.…”

Section: Fp Tpmentioning

confidence: 99%

“…Compared with the two-dimensional image feature extraction method, the threedimensional point cloud feature extraction method is a recent innovation. How to realize an efficient and robust point cloud geometric feature extraction algorithm has been a hot issue in this field in recent years [9][10][11][12][13][14]. Many scholars have extracted the geometric features of point clouds through traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

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“…These are all the new developments in the study of building density. (4) Data visualization: Urban development today requires not only a one-time investigation but also the dynamics, namely, various quantitative information on BD changes; for example, LiDAR and GEOBIA data can be used to measure spatial-temporal changes in 3D building density [28]; high-resolution images can analyze changes in building density in Shanghai [21], and high-resolution multi-view satellites can observe subtle changes in China's megacities [29]. However, few studies have been reported in this field.…”

Section: Introductionmentioning

confidence: 99%

Building Density Dynamics and Habitability Evaluation of China’s Nanning City

Yang

Jia

et al. 2023

Sustainability

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Unlocking the secrets of habitable urban areas is crucial to improve the quality of life for urban dwellers. Accurate assessment of the ever-changing dynamics of a modern metropolis remains a challenging task. Previous studies have failed to reveal the dynamics of urban building spatial configuration at the micro-level. By analyzing high-resolution satellite imagery, this study has developed new direct and indirect metrics to better understand building density dynamics. We also applied these metrics to a study area located in Nanning City, China, revealing fascinating insights into the evolving spatial patterns of building density over the past 17 years. Our Q/R analysis uncovered areas with high habitability and suggested strategic improvements for sustainable building spatial configuration. This study is a valuable addition to the growing body of urban development research and provides scientific references for measurements of sustainable urban planning worldwide.

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Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

Cited by 6 publications

References 46 publications

Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation

Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Building Density Dynamics and Habitability Evaluation of China’s Nanning City

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