A remote sensing extraction method for building contours based on YOLO and CBAM model fusion

doi:10.13474/j.cnki.11-2246.2026.0720

Abstract

Abstract: [Purposes] Remote sensing extraction of building outlines serves as a vital source of foundational data for natural resource surveys and spatial planning.Addressing the challenge of low extraction accuracy,stemming from the complex geometric characteristics of building contours in remote sensing imagery,this study proposes the YOLO-CBAM model,which integrates the YOLO11 framework with the convolutional block attention module (CBAM)for enhanced remote sensing building contour extraction. [Methods] The model is trained and validated using the public GF-7 building dataset and compared against YOLOv8,YOLO11,the YOLO-CA model (incorporating the Coordinate Attention mechanism with YOLO11),U-Net,DeepLabV3,and Mask-RCNN. [Findings] Experimental results demonstrate that the YOLO-CBAM model achieves IoU,Dice index,mAP@0.5,F₁ score,maximum recall(R) values of 71.1%,81.5%,74.7%,0.72,88.0%,respectively. [Conclusions] These evaluation metrics outperform those of YOLOv8,YOLO11,YOLO-CA,U-Net,DeepLabV3,and Mask-RCNN confirming that the integration of CBAM significantly improves the accuracy of YOLO11 in remote sensing-based building contour extraction.

Key words: YOLO model, building contours, high-resolution remote sensing, attention mechanism

CLC Number:

P237

Mao Yaqin, Ding Xiaohui, Tu Liping, Fan Junlin, He Yuyue, Xu Ronghua. A remote sensing extraction method for building contours based on YOLO and CBAM model fusion[J]. Bulletin of Surveying and Mapping, 2026, 0(7): 136-141.

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http://tb.chinasmp.com/EN/Y2026/V0/I7/136

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