Cascade DeepLab Net: A Method for Accurate Extraction of Fragmented Cultivated Land in Mountainous Areas Based on a Cascaded Network

Man Li, Renru Wang, Ana Dai, Weitao Yuan (), Guangbin Yang (), Lijun Xie, Weili Zhao and Linglin Zhao
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Man Li: School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Renru Wang: School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Ana Dai: Guizhou Provincial First Surveying and Mapping Institute, Guiyang 550025, China
Weitao Yuan: Guizhou Provincial First Surveying and Mapping Institute, Guiyang 550025, China
Guangbin Yang: School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Lijun Xie: School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Weili Zhao: Guizhou Provincial First Surveying and Mapping Institute, Guiyang 550025, China
Linglin Zhao: School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China

Agriculture, 2025, vol. 15, issue 3, 1-20

Abstract: Approximately 24% of the global land area consists of mountainous regions, with 10% of the population relying on these areas for their cultivated land. Accurate statistics and monitoring of cultivated land in mountainous regions are crucial for ensuring food security, creating scientific land use policies, and protecting the ecological environment. However, the fragmented nature of cultivated land in these complex terrains challenges the effectiveness of existing extraction methods. To address this issue, this study proposed a cascaded network based on an improved semantic segmentation model (DeepLabV3+), called Cascade DeepLab Net, specifically designed to improve the accuracy in the scenario of fragmented land features. This method aims to accurately extract cultivated land from remote sensing images. This model enhances the accuracy of cultivated land extraction in complex terrains by incorporating the Style-based Recalibration Module (SRM), Spatial Attention Module (SAM), and Refinement Module (RM). The experimental results using high-resolution satellite images of mountainous areas in southern China show that the improved model achieved an overall accuracy (OA) of 92.33% and an Intersection over Union (IoU) of 82.51%, marking a significant improvement over models such as U-shaped Network (UNet), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+. This method enhances the efficiency and accuracy of monitoring cultivated land in mountainous areas and offers a scientific basis for policy formulation and resource management, aiding in ecological protection and sustainable development. Additionally, this study presents new ideas and methods for future applications of cultivated land monitoring in other complex terrain regions.

Keywords: cultivated land extraction; mountainous areas; fragmented land; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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