Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features
文献类型：期刊论文
| 作者 | Zhang, Die1,2; Liu, Xin3; Ge, Yong1,2,4; Wei, Yixi5; Liu, Mengxiao6 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 18期号:1页码:2512410 |
| 关键词 | Indoor/outdoor differentiation building matching data-driven positioning spatial analysis location recognition |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2025.2512410 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| WOS关键词 | INDOOR ; SMARTPHONES ; CHALLENGES |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001500944300001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214566]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Ge, Yong |
| 作者单位 | 1.Jiangxi Normal Univ, Sch Geog & Environm, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Peoples R China; 2.Jiangxi Normal Univ, Jiangxi Prov Key Lab Ecol Intelligent Monitoring &, Nanchang, Peoples R China; 3.Huawei Technol Co Ltd, Lab 2012, Shenzhen, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 5.Xian Med Univ, Dept Math, Xian, Peoples R China; 6.Chinese Res Inst Environm Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Die,Liu, Xin,Ge, Yong,et al. Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):2512410. |
| APA | Zhang, Die,Liu, Xin,Ge, Yong,Wei, Yixi,&Liu, Mengxiao.(2025).Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),2512410. |
| MLA | Zhang, Die,et al."Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):2512410. |
入库方式： OAI收割
来源：地理科学与资源研究所
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