Parcel Segmentation Method Combined YOLOV5s and Segment Anything Model Using Remote Sensing Image

Xiaoqin Wu, Dacheng Wang, Caihong Ma (), Yi Zeng, Yongze Lv, Xianmiao Huang and Jiandong Wang
Additional contact information
Xiaoqin Wu: College of Information, Beijing Forestry University, Beijing 100091, China
Dacheng Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Caihong Ma: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yi Zeng: College of Information, Beijing Forestry University, Beijing 100091, China
Yongze Lv: College of Information, Beijing Forestry University, Beijing 100091, China
Xianmiao Huang: College of Information, Beijing Forestry University, Beijing 100091, China
Jiandong Wang: College of Information, Beijing Forestry University, Beijing 100091, China

Land, 2025, vol. 14, issue 7, 1-22

Abstract: Accurate land parcel segmentation in remote sensing imagery is critical for applications such as land use analysis, agricultural monitoring, and urban planning. However, existing methods often underperform in complex scenes due to small-object segmentation challenges, blurred boundaries, and background interference, often influenced by sensor resolution and atmospheric variation. To address these limitations, we propose a dual-stage framework that combines an enhanced YOLOv5s detector with the Segment Anything Model (SAM) to improve segmentation accuracy and robustness. The improved YOLOv5s module integrates Efficient Channel Attention (ECA) and BiFPN to boost feature extraction and small-object recognition, while Soft-NMS is used to reduce missed detections. The SAM module receives bounding-box prompts from YOLOv5s and incorporates morphological refinement and mask stability scoring for improved boundary continuity and mask quality. A composite Focal-Dice loss is applied to mitigate class imbalance. In addition to the publicly available CCF BDCI dataset, we constructed a new WuJiang dataset to evaluate cross-domain performance. Experimental results demonstrate that our method achieves an IoU of 89.8% and a precision of 90.2%, outperforming baseline models and showing strong generalizability across diverse remote sensing conditions.

Keywords: remote-sensing images; parcel segmentation; YOLOv5s; SAM (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/7/1429/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/7/1429/ (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:14:y:2025:i:7:p:1429-:d:1696710

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 ().