A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2388
- Type
- D - Journal article
- DOI
-
10.3390/rs12061032
- Title of journal
- Remote Sensing
- Article number
- 1032
- First page
- -
- Volume
- 12
- Issue
- 6
- ISSN
- 2072-4292
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- URL
-
https://e-space.mmu.ac.uk/627176/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
A - Data Science
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper provides a novel solution for using multi-source data to understand regional functions and their relation to human activities, which is important for urban planning and the improvement of wellbeing but is a technical limitation in exiting methods. An empirical evaluation reveals significant benefits in both the precision and computational stability of the proposed solution. The paper forms the basis for continuing collaboration, involving 5 research students, an exchange student funded by the China Scholarship Council (Ms Jie SU, No.201703780125), and a successful NSFC award (No. 61871278), which is exploring data-enabled approaches for urban planning in Western China.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -