A Multi-Scale Residential Areas Matching Method Considering Spatial Neighborhood Features
Bibliographic Data
| ID | 22034650 |
|---|---|
| Authors | Jingzhen Ma (0000-0002-2195-0131, PLA Information Engineering University), Qun Sun (0000-0002-4372-8865, PLA Information Engineering University), Zhao Zhou (0000-0003-2840-7155, PLA Information Engineering University), Bowei Wen (PLA Information Engineering University), Shaomei Li (0000-0002-8010-1850, PLA Information Engineering University, corresponding author) |
| Year | 2022 |
| Volume | 11 |
| Issue | 6 |
| Pages | 331 |
| Publication date | 2022-05-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11060331 |
| OpenAlex | W4281801092 |
| Language | EN |
| Citations received | 3 |
| References cited | 14 |
Residential areas is one of the basic geographical elements on the map and an important content of the map representation. Multi-scale residential areas matching refers to the process of identifying and associating entities with the same name in different data sources, which can be widely used in map compilation, data fusion, change detection and update. A matching method considering spatial neighborhood features is proposed to solve the complex matching problem of multi-scale residential areas. The method uses Delaunay triangulation to divide complex matching entities in different scales into closed domains through spatial neighborhood clusters, which can obtain many-to-many matching candidate feature sets. At the same time, the geometric features and topological features of the residential areas are fully considered, and the Relief-F algorithm is used to determine the weight values of different similarity features. Then the similarity and spatial neighborhood similarity of the polygon residential areas are calculated, after which the final matching results are obtained. The experimental results show that the accuracy rate, recall rate and F value of the matching method are all above 90%, which has a high matching accuracy. It can identify a variety of matching relationships and overcome the influence of certain positional deviations on matching results. The proposed method can not only take account of the spatial neighborhood characteristics of residential areas, but also identify complex matching relationships in multi-scale residential areas accurately with a good matching effect
Algorithm · Cartography · Data mining · Delaunay triangulation · Geography · Optimal matching · Statistics · Automated Road and Building Extraction · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Mathematics · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |