U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model

Lirong Yin, Lei Wang, Tingqiao Li, Siyu Lu, Zhengtong Yin, Xuan Liu, Xiaolu Li and Wenfeng Zheng ()
Additional contact information
Lirong Yin: Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Lei Wang: Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Tingqiao Li: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Siyu Lu: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Zhengtong Yin: College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China
Xuan Liu: School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
Xiaolu Li: School of Geographical Sciences, Southwest University, Chongqing 400715, China
Wenfeng Zheng: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

Land, 2023, vol. 12, issue 8, 1-23

Abstract: Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.

Keywords: U-Net; deep learning; land use/land cover change; remote sensing; Lake Urmia; CNN; STN (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/8/1602/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/8/1602/ (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:jlands:v:12:y:2023:i:8:p:1602-:d:1217169

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

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