Land Use Simulation of Shanghai Based on Multi-source Data Integration
Authors
Yundan Bai1, 2, 3, Fayun Li1, 2, *, Weiyu Yu1, 2
1School of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
2Department Institute of Beautiful China and Ecological Civilization, University Think Tank of Shanghai Municipality, Shanghai, 201418, China
3Institute of Oriental Language and Culture, Shanghai Vocational College of Business and Foreign Languages, Shanghai, 201399, China
*Corresponding author.
Email: lifayun@sit.edu.cn
Corresponding Author
Fayun Li
Available Online 24 April 2024.
- DOI
- 10.2991/978-94-6463-398-6_65How to use a DOI?
- Keywords
- multi-source data; land use simulation; integration
- Abstract
With the continuous improvement of big data mining technology, massive and multi-source land use spatio-temporal big data began to emerge. Big data thinking plays an increasingly important role in ecological monitoring, smart city construction, public safety and support for major decisions. Taking Shanghai as an example, this paper integrates ESA CCI, GlobeLand 30 and MODIS data by voting method, and inputs MCE-CA-Markov model to obtain more accurate prediction results. This study provides a new simulation thinking, which can provide ideas for the prediction of Shanghai and other similar cities.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yundan Bai AU - Fayun Li AU - Weiyu Yu PY - 2024 DA - 2024/04/24 TI - Land Use Simulation of Shanghai Based on Multi-source Data Integration BT - Proceedings of the 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023) PB - Atlantis Press SP - 666 EP - 673 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-398-6_65 DO - 10.2991/978-94-6463-398-6_65 ID - Bai2024 ER -