Spatial context-aware method for urban land use classification using street view images

Fang, Fang; Zeng, Linyun; Li, Shengwen; Zheng, Daoyuan; Zhang, Jiahui; Liu, Yuanyuan; Wan, Bo

doi:10.1016/j.isprsjprs.2022.07.020

Cited by 25 publications

(11 citation statements)

References 31 publications

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“…SVI is an ideal dataset to comprehensively describe the urban environmental variability. For example, it has been used to model buildings (Gurney et al, 2012) including building height (Yan and Huang, 2022), streetscape features (Wang, Liu and Gou, 2022), green and water systems (Jiang, Jiang and Shi, 2020), land use classification (Jain, Meiyappan and Richardson, 2013;Tian, Han and Xu, 2021;Fang et al, 2022), the openness (Xia, Yabuki and Fukuda, 2021), road network (Zhang et al, 2023), mobile monitoring (Sun et al, 2017) and POI (Gao, Janowicz and Couclelis, 2017;Huang et al, 2022;Song et al, 2022;X. Xu, Qiu, Li, Liu, et al, 2022).…”

Section: Street View Image and Ai To Model Urban Formsmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

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Section: Street View Image and Ai To Model Urban Formsmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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“…Thanks to the emergence of geospatial big data collected from location-based services (LBSs) and the Internet of Things (IoT), an increasing number of researchers have taken advantage of GCNNs to investigate urban issues, e.g., traffic prediction [43,44], urban land-use recognition [24,45], urban scene classification [25], urban security perception [46], public health evaluation [47], weather forecasting [48], and cultural association mining [49]. Nevertheless, the implicit semantics and contextual information of geospatial big data are underexploited in the identification of urban functional features.…”

Section: Place Embedding With Graph Convolutional Neural Networkmentioning

confidence: 99%

Semantic-Enhanced Graph Convolutional Neural Networks for Multi-Scale Urban Functional-Feature Identification Based on Human Mobility

Chen,

Zhao,

Lin

et al. 2024

IJGI

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Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs’ performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method.

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“…YPERSPECTRAL remote sensing has been widely used in many industries [1]- [8], and the main challenges still faced by hyperspectral remote sensing image classification [9]- [13] as one of the main applications are the high dimensionality of the data [14], the small number of labeled samples and the difficulty of their collection [15][16] [17], and the problem of spatial homogeneity and heterogeneity of classification results [18]- [23].…”

Section: Introductionmentioning

confidence: 99%

Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint Classification for Hyperspectral Images

Su,

Shi,

Xue

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Collaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most collaborative representation classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and heterogeneity problems. Therefore, this paper proposes a mean-weighted collaborative representation classification model (MWCRC) based on the joint spatial-spectral data. It imposes mean and weighted constraints on the representation coefficients based on CR, which attenuates the noise effect and increases the distinguishability between classes. Second, a sample augmentation method based on the principle of minimizing the representation residuals is proposed. Sample augmentation is realized through initial classification and calculation of representation residuals to achieve the objective of consolidating model stability and improving classification accuracy. Meanwhile, in order to alleviate the problem of spatial homogeneity and heterogeneity, the extended morphological profile (EMP) and the stacking approach are utilized to construct the joint spatial-spectral data for the classification of MWCRC. The superiority of the proposed method is demonstrated by experimental validation using a small number of training samples in three real datasets.

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Spatial context-aware method for urban land use classification using street view images

Cited by 25 publications

References 31 publications

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Semantic-Enhanced Graph Convolutional Neural Networks for Multi-Scale Urban Functional-Feature Identification Based on Human Mobility

Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint Classification for Hyperspectral Images

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