Classifying urban land use by integrating remote sensing and social media data

Liu, Xiaoping; He, Jialyu; Yao, Yao; Zhang, Jinbao; Liang, Haolin; Wang, Huan; Hong, Ye

doi:10.1080/13658816.2017.1324976

Cited by 278 publications

(193 citation statements)

References 57 publications

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“…MPPD are a reliable indicator of the patterns of human mobility, and they have been shown to be remarkably beneficial in land use mapping [25,30]. However, an inherent weakness of MPPD is the inability to distinguish several land use classes, e.g., grassland and bare-land.…”

Section: Classification Of Mppdmentioning

confidence: 99%

“…When fusion is performed, a key step is to aggregate the social data into a certain spatial unit, and different aggregation methods might be used for these various social sensing data [25,40,42,43]. For the POIs data, spatial distribution density of POIs by category is the prevalent way to express social features with a certain spatial unit [30]. For the check-in data, the amount of check-in data within a spatial unit should be aggregated according to the category and period [40].…”

Section: Advantages Of the Fused Land Cover Map And Future Workmentioning

confidence: 99%

“…Several alternative sources of social sensing data, including POIs [30], check-in data with location information [40,41], mobile phone data [25], and trajectory data [42], can then be applied to replace the MPPD data. A similar approach that fuses remote sensing and various social sensing data can be used to obtain a land use map.…”

Section: Advantages Of the Fused Land Cover Map And Future Workmentioning

confidence: 99%

“…Liu et al combined spectral, textural, and spatial features calculated from high-resolution RSI and social features derived from mobile phone positioning data (MPPD) to identify land use types of different scenes. Compared with other socially sensed data, such as POI and mobile phone data, MPPD are most suitable for land use mapping, because this type of data has the advantage of expressing human mobility [30]. Although high-spatial-resolution RSI has shown potential for use in land use mapping when combined with social features, medium-spatial-resolution RSI is easier to acquire with little cost and appropriate for large-area mapping.…”

Section: Introductionmentioning

confidence: 99%

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Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

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Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

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Section: Classification Of Mppdmentioning

confidence: 99%

Section: Advantages Of the Fused Land Cover Map And Future Workmentioning

confidence: 99%

Section: Advantages Of the Fused Land Cover Map And Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

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“…Then, the generated zones are classified into different functional categories by using the scene classification method [8], which is trained based on supervised samples and sorts zones by considering their intra-scene feature similarity and inter-scene semantic dependency. The scene classification method can analyze heterogeneous urban functional zones and can produce accurate classification results [49]. Firstly, 82 zones are selected as training samples and they are manually labeled based on field investigations and a city-planning map.…”

Section: Process Of Urban-functional-zone Mappingmentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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Resilience and green spaces: Association with stress among contact centre workers in the Philippines

Villanueva

Labao

Tran

et al. 2023

Health Prom J of Aust

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Issue AddressedPhilippine contact centres are rife with factors that contribute to work‐related stress; health promotion strategies are needed to mitigate the impacts. With a transactional framework with the environment, this study examined the relationship of stress with resilience and the presence of urban green spaces (UGS) in the environment, while accounting for individual characteristics (ie, age, household income, exercise frequency).MethodsParticipants include employees (Stage 1 N = 186; Stage 2 N = 89) from six contact centres in the capital region of the Philippines. A two‐stage online survey included standardised instruments to measure stress (10‐item Perceived Stress Scale) and resilience (Connor‐Davidson Resilience Scale), customised questions to gather demographic information and probe on participants' insights. Google Earth Pro was used for satellite mapping of UGS, followed by on‐site ocular inspection.ResultsParticipants' average stress level was categorised as high; primary stressors included client demands and workload. The objectively measured percentages of UGS in the study sites' vicinity were categorised as low. Participants found UGS visible after careful observation, and majority were aware of UGS in their workplace vicinity. Resilience, household income and awareness of UGS in the vicinity significantly predicted stress levels.ConclusionContact centre workers experienced high stress levels and their workplaces had little accessible UGS. Resilience, household income and awareness of UGS are significant contributors to stress levels.So What?Health promotion in Philippine contact centres could consider strategies that include building resilience, enhancing income security and promoting the awareness of UGS within the workplace vicinity.

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Classifying urban land use by integrating remote sensing and social media data

Cited by 278 publications

References 57 publications

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Resilience and green spaces: Association with stress among contact centre workers in the Philippines

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