Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features
As individuals spend most of their time indoors, determining whether a mobile device is located indoors or outdoors – and identifying the specific building it is in – is essential for enabling building-level location-based services and fine-grained human activity analysis. However, existing indoor p...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2512410 |
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| _version_ | 1849224290890678272 |
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| author | Die Zhang Xin Liu Yong Ge Yixi Wei Mengxiao Liu |
| author_facet | Die Zhang Xin Liu Yong Ge Yixi Wei Mengxiao Liu |
| author_sort | Die Zhang |
| collection | DOAJ |
| description | As individuals spend most of their time indoors, determining whether a mobile device is located indoors or outdoors – and identifying the specific building it is in – is essential for enabling building-level location-based services and fine-grained human activity analysis. However, existing indoor positioning techniques often rely on dedicated infrastructure or dense signal fingerprinting, limiting their scalability across diverse urban environments. To address this, we propose a lightweight, data-driven framework for building-level mobile device location recognition that integrates indoor/outdoor (I/O) classification and building matching using limited sensor data. A random forest model is trained on a structured, scene-diverse sample library to classify I/O status based on multi-sensor features. For devices identified as indoors, building identification is performed using a Bayesian inference model that incorporates prior knowledge derived from anonymous crowdsourced data, leveraging spatial heterogeneity in sensor feature distributions across candidate buildings. Experiments conducted in three Chinese cities demonstrated that I/O classification achieved over 90% accuracy, and building matching based on crowdsourced data achieved at least 70% precision using only satellite or Wi-Fi features. Our approach requires no infrastructure deployment or extensive labeled data, offering a scalable and practical solution for building-level location inference across large and heterogeneous regions. |
| format | Article |
| id | doaj-art-57908f5ef73843dc97dd5494aa6631fc |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-57908f5ef73843dc97dd5494aa6631fc2025-08-25T11:28:25ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2512410Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor featuresDie Zhang0Xin Liu1Yong Ge2Yixi Wei3Mengxiao Liu4School of Geography and Environment / Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang, People’s Republic of China2012 Lab, Huawei Technologies Co. Ltd., Shenzhen, People’s Republic of ChinaSchool of Geography and Environment / Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang, People’s Republic of ChinaDepartment of Mathematics, Xi'an Medical University, Xi'an, People’s Republic of ChinaChinese Research Academy of Environmental Sciences, Beijing, People’s Republic of ChinaAs individuals spend most of their time indoors, determining whether a mobile device is located indoors or outdoors – and identifying the specific building it is in – is essential for enabling building-level location-based services and fine-grained human activity analysis. However, existing indoor positioning techniques often rely on dedicated infrastructure or dense signal fingerprinting, limiting their scalability across diverse urban environments. To address this, we propose a lightweight, data-driven framework for building-level mobile device location recognition that integrates indoor/outdoor (I/O) classification and building matching using limited sensor data. A random forest model is trained on a structured, scene-diverse sample library to classify I/O status based on multi-sensor features. For devices identified as indoors, building identification is performed using a Bayesian inference model that incorporates prior knowledge derived from anonymous crowdsourced data, leveraging spatial heterogeneity in sensor feature distributions across candidate buildings. Experiments conducted in three Chinese cities demonstrated that I/O classification achieved over 90% accuracy, and building matching based on crowdsourced data achieved at least 70% precision using only satellite or Wi-Fi features. Our approach requires no infrastructure deployment or extensive labeled data, offering a scalable and practical solution for building-level location inference across large and heterogeneous regions.https://www.tandfonline.com/doi/10.1080/17538947.2025.2512410Indoor/outdoor differentiationbuilding matchingdata-driven positioningspatial analysislocation recognition |
| spellingShingle | Die Zhang Xin Liu Yong Ge Yixi Wei Mengxiao Liu Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features International Journal of Digital Earth Indoor/outdoor differentiation building matching data-driven positioning spatial analysis location recognition |
| title | Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features |
| title_full | Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features |
| title_fullStr | Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features |
| title_full_unstemmed | Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features |
| title_short | Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features |
| title_sort | urban building level positioning using data driven algorithms enhanced by spatial variations in sensor features |
| topic | Indoor/outdoor differentiation building matching data-driven positioning spatial analysis location recognition |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2512410 |
| work_keys_str_mv | AT diezhang urbanbuildinglevelpositioningusingdatadrivenalgorithmsenhancedbyspatialvariationsinsensorfeatures AT xinliu urbanbuildinglevelpositioningusingdatadrivenalgorithmsenhancedbyspatialvariationsinsensorfeatures AT yongge urbanbuildinglevelpositioningusingdatadrivenalgorithmsenhancedbyspatialvariationsinsensorfeatures AT yixiwei urbanbuildinglevelpositioningusingdatadrivenalgorithmsenhancedbyspatialvariationsinsensorfeatures AT mengxiaoliu urbanbuildinglevelpositioningusingdatadrivenalgorithmsenhancedbyspatialvariationsinsensorfeatures |