MSRI: A Minimum Spatial Residual Iterative Algorithm for Vector Spatial Data Matching Based on Distance Decay
文献类型：期刊论文
| 作者 | Huang, Kejia2,7,8; Chen, Taisheng1,6; Muhammad, Niaz2,7,8; Shi, Wenjiao5; Liu, Di4; Liu, Rongsheng3 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 2025-10-06 |
| 卷号 | 29期号:7页码:e70128 |
| 关键词 | distance decay model geometric similarity MSRI spatial residual minimization vector spatial data matching |
| ISSN号 | 1361-1682 |
| DOI | 10.1111/tgis.70128 |
| 产权排序 | 6 |
| 文献子类 | Article |
| 英文摘要 | With the rapid advancement of geographic information technologies, multi-source Vector Spatial Data have become increasingly accessible; however, achieving robust and automated matching remains a significant challenge due to structural inconsistencies and sensor misalignments. This study introduces the Minimum Spatial Residual Iterative (MSRI) algorithm, inspired by Tobler's Second Law of Geography, which integrates a spatial decay model with a residual-based weighting scheme to guide iterative vector matching. Extensive experiments show that MSRI consistently outperforms classical methods such as Iterative Closest Point (ICP), Trimmed ICP (TrICP), and RANSAC + FPFH, particularly under irregular displacements, occlusions, and high data incompleteness. MSRI achieves an RMSE of 0.54 m, compared to 1.26-1.83 m for baselines, and improves F1-scores by 12%-30%. Even with 70% data loss, MSRI maintains stable accuracy and reduces RMSE by up to 3.7x relative to TrICP, while ICP and RANSAC often fail to converge. A sensitivity analysis further confirms stable performance across practical parameter ranges, with optimal results obtained at alpha = 0.4 and beta = 0.6. With its robustness, automation, and minimal preprocessing requirements, MSRI provides a practical solution for applications such as smart city construction, environmental monitoring, and large-scale vector spatial data integration. |
| URL标识 | 查看原文 |
| WOS关键词 | GEOSPATIAL DATA ; INFORMATION ; MULTISOURCE ; INTEGRATION ; FUSION ; SPACE |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001588495600001 |
| 出版者 | WILEY |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217436]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Liu, Di |
| 作者单位 | 1.Suzhou Univ Sci & Technol, Sch Geog Sci & Geomat Engn, Suzhou, Jiangsu, Peoples R China; 2.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing, Peoples R China; 3.Hefei Yunchuang Space Informat Technol Co Ltd, Hefei, Anhui, Peoples R China 4.Sun Yat Sen Univ, Sch Aeronaut & Astronaut, Shenzhen Campus, Shenzhen, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 6.Suzhou Key Lab Spatial Informat Intelligent Techno, Suzhou, Peoples R China; 7.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Jiangsu, Peoples R China; 8.Nanjing Normal Univ, Sch Geog, Nanjing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Huang, Kejia,Chen, Taisheng,Muhammad, Niaz,et al. MSRI: A Minimum Spatial Residual Iterative Algorithm for Vector Spatial Data Matching Based on Distance Decay[J]. TRANSACTIONS IN GIS,2025,29(7):e70128. |
| APA | Huang, Kejia,Chen, Taisheng,Muhammad, Niaz,Shi, Wenjiao,Liu, Di,&Liu, Rongsheng.(2025).MSRI: A Minimum Spatial Residual Iterative Algorithm for Vector Spatial Data Matching Based on Distance Decay.TRANSACTIONS IN GIS,29(7),e70128. |
| MLA | Huang, Kejia,et al."MSRI: A Minimum Spatial Residual Iterative Algorithm for Vector Spatial Data Matching Based on Distance Decay".TRANSACTIONS IN GIS 29.7(2025):e70128. |
入库方式： OAI收割
来源：地理科学与资源研究所
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