SWLS：沙湾市遥感图像土地覆盖语义分割数据集

卢毅果; 钱育蓉; 白璐; 范迎迎; 李志财; 刘鹏

doi:10.13568/j.cnki.651094.651316.2024.12.29.0002

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SWLS：沙湾市遥感图像土地覆盖语义分割数据集

空-天-地一体化智能数字基座关键技术研究与行业应用专题 | 更新时间：2026-01-22

- SWLS：沙湾市遥感图像土地覆盖语义分割数据集
- SWLS：沙湾市遥感图像土地覆盖语义分割数据集
- 新疆大学学报（自然科学版中英文） 2025年42卷第4期页码：434-443
- 作者机构：
  
  1. 新疆大学软件学院新疆大数据与智能软件工程研究中心
  2. 新疆大学软件工程重点实验室
  3. 新疆大学计算机科学与技术学院
  4. 丝路多语言认知计算国际合作联合实验室
- 作者简介：
- 基金信息：
  
  新疆维吾尔自治区杰出青年科学基金“基于多时相协同变化检测的基本农田遥感监测研究”（2023D01E01）;国家自然科学基金“面向数字孪生农业的空-天-地多模态数据融合关键技术研究”（62266043）;新疆维吾尔自治区青年拔尖人才项目“基于多源遥感大数据的粮食安全关键技术研究”（2023TSYCCX0043）;新疆维吾尔自治区天山创新团队项目“面向农业的天地协同水资源时空精准调度研究及应用”（2023D14012）;新疆维吾尔自治区重点研发专项“边防异常信息多元感知与智能监测研究”（2023B01029-1,2023B01029-2）;新疆维吾尔自治区自然科学基金“基于深度学习的多源数据融合技术在新疆棉田识别与产量预测中的应用研究”（2022D01B123）;国防科工局高分辨率对地观测系统重大专项“高分专项沙湾市乡村振兴产业化示范”（95-Y50G37-9001-22/23）
- DOI：10.13568/j.cnki.651094.651316.2024.12.29.0002
  中图分类号： TP751
- 纸质出版：2025
- 稿件说明：
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[1]卢毅果,钱育蓉,白璐,等.SWLS：沙湾市遥感图像土地覆盖语义分割数据集[J].新疆大学学报(自然科学版中英文),2025,42(04):434-443.
[1]卢毅果,钱育蓉,白璐,等.SWLS：沙湾市遥感图像土地覆盖语义分割数据集[J].新疆大学学报(自然科学版中英文),2025,42(04):434-443. DOI： 10.13568/j.cnki.651094.651316.2024.12.29.0002.

DOI：10.13568/j.cnki.651094.651316.2024.12.29.0002.

摘要

近年来，随着国产高分卫星数据获取的便利性提升，遥感图像在城市扩展监测、水资源管理及农业应用等领域的解译需求愈发迫切，遥感图像语义分割技术逐渐成为核心手段之一，高质量的基准数据集是训练和评估遥感图像语义分割算法的基础．现有土地覆盖语义分割数据集大多缺乏近红外波段，地物标注精细度和多样性不足，且缺少新疆特有地貌的数据支持，限制了模型在特定地区实际应用的效果．为此，提出了一个专注于新疆沙湾市的高分辨率多光谱卫星遥感图像土地覆盖语义分割数据集Shawan Land-cover Semantic Segmentation Dataset(SWLS)，该数据集基于高分一号卫星（GF-1）的多光谱图像，涵盖了RGB和近红外4个波段，具有更精细的地物标注，尤其在防风林和建筑物等复杂类别上具备差异化标注．在SWLS数据集上对多种经典卷积神经网络和Transformer架构的语义分割模型进行了全面评估，卷积架构模型最高m IoU为87.46%

Transformer架构最高m IoU为69.83%.

Abstract

In recent years

with the increasing accessibility of high-resolution domestic satellite data

the demand for interpreting remote sensing images in areas such as urban expansion monitoring

water resource management

and agricultural applications has grown significantly. Semantic segmentation of remote sensing images has gradually become one of the core techniques in this field. High-quality benchmark dataset is fundamental for training and evaluating semantic segmentation algorithms. However

existing land cover semantic segmentation datasets often lack near-infrared bands

have limited annotation precision and diversity

and do not include data specific to the unique geomorphology of Xinjiang of China

thereby limiting the effectiveness of models in practical applications in specific regions. To this end

the Shawan Land-cover Semantic Segmentation Dataset(SWLS) has been introduced.This dataset focuses on Shawan in Xinjiang and consists of high-resolution multispectral satellite remote sensing images acquired by the Gaofen-1(GF-1) satellite. It includes four bands: RGB and near-infrared

and features more detailed land cover annotations

particularly offering differentiated labeling for complex categories such as shelter forests and buildings. A comprehensive evaluation was conducted on the SWLS dataset using various classical Convolutional Neural Network(CNN) and Transformer-based semantic segmentation models. Among them

the best-performing CNN model achieved a maximum mean Intersection over Union(m IoU) of 87.46%

while the best Transformer-based model reached a maximum m IoU of 69.83%.

关键词

Keywords

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