MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM

Yijiang Zhao, Yahan Ning, Haodong Li, Zhuhua Liao, Yizhi Liu () and Feng Li ()
Additional contact information
Yijiang Zhao: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Yahan Ning: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Haodong Li: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Zhuhua Liao: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Yizhi Liu: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

Sustainability, 2023, vol. 15, issue 24, 1-17

Abstract: The quality of OpenStreetMap (OSM) has been widely concerned as a valuable source for monitoring some sustainable development goals (SDG) indicators. Improving its semantic quality is still challenging. As a kind of solution, road type prediction plays an important role. However, most existing algorithms show low accuracy, owing to data sparseness and inaccurate description. To address these problems, we propose a novel OSM road type prediction approach via integrating multiple spatial contexts with DeepFM, named MSC-DeepFM. A deep learning model DeepFM is used for dealing with data sparseness. Moreover, multiple spatial contexts (MSC), including the features of intersecting roads, surrounding buildings, and points of interest (POIs), are distilled to describe multiple types of road more accurately. The MSC combined with geometric features and restricted features are put into DeepFM, in which the low-order and high-order features fully interact. And a multivariate classifier OneVsRest is adopted to predict road types. Experiments on OSM show that the proposed model MSC-DeepFM achieves excellent performance and outperforms some state-of-the-art methods.

Keywords: road type prediction; multiple spatial contexts (MSC); DeepFM; OpenStreetMap; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/24/16671/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/24/16671/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:24:p:16671-:d:1296508

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().