A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

Wu, Lun; Cheng, Ximeng; Kang, Chaogui; Zhu, Daoyun; Huang, Zhou; Liu, Yu

doi:10.1080/17538947.2018.1556353

Cited by 45 publications

(10 citation statements)

References 42 publications

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“…Much of this big data are geo-referenced, or can be geo-referenced, leading to geospatial big data (GBD). The emergence of GBD [9], such as mobile phone positioning data [10,11], points-of-interest (POI) [12], social media data [13][14][15], traffic trajectory data [16], and geotagged photographs [17][18][19], provides new opportunities to delineate human dimensions in an urban environment [20]. These multi-sourced data contain abundant human activity information, compensating for the lack of socioeconomic attributes of the RS data [21].…”

Section: Introductionmentioning

confidence: 99%

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Yin

Hamm

et al. 2021

Remote Sensing

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Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

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Section: Introductionmentioning

confidence: 99%

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Yin

Hamm

et al. 2021

Remote Sensing

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“…It provides an essential tool for examining the way social, economic and ecological factors shape the spatial structure and change of urban processes under both empirical and simulation scenarios [1].State-of-the-art approaches heavily rely on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classifications. Examples include the derivation of the physical characteristics (i.e., urban structure) of the built-up environments from remote sensing imagery [2], and the extraction of spatiotemporal characteristics (i.e., urban dynamics) from social sensing data [3], in order to determine the functional characteristics (i.e., land use) of urban areas [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

Huang

Kang

et al. 2020

Remote Sensing

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Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

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“…However, the functional zone maps are still hardly available in many cities (Zhang, Du, & Wang, 2017). The recognition of urban functional zones is helpful for understanding the spatial structure of a city and guiding the configuration of resources, which improves the governance mechanism for urban management and urban planning (Wu et al, 2020). Numerous methods have been developed to cognize the functional zones based on remote sensing images and crowdsourced data (such as points‐of‐interest [POIs] data).…”

Section: Introductionmentioning

confidence: 99%

Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities

Liu

Tang

et al. 2020

Transactions in GIS

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Functional zones are basic spatial units of urban management and urban planning and they provide the basic space where human activities occur. The concept of urban functional zones is closely related to two basic factors: urban landscape environment and human activities. The former provides the basic space for human activity and influences urban land use at a coarse scale, and the latter indicates the differentiation of functions in local urban areas. In previous

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A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

Cited by 45 publications

References 42 publications

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities

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