从高分辨率卫星图像中获得的中国北京两个地区的建筑垃圾填埋场数据集
Shaofu Lin1, Lei Huang1, Xiliang Liu2
1Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, 100124, China.
Scientific data
|April 16, 2024
概括
这项研究创建了一个新的数据集,用于使用卫星图像识别建筑垃圾填埋场. 该资源有助于开发深度学习模型,以准确量化和管理废物.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 城市发展产生不可避免的建筑垃圾,导致严重的环境污染.
- 准确评估建筑垃圾需要强大的识别模型和高质量的数据集进行培训和验证.
- 现有的数据集可能无法充分满足建筑垃圾填埋场识别的特定需求.
研究的目的:
- 使用高分辨率卫星图像构建用于建筑垃圾填埋场识别的专用数据集.
- 提供像素级的语义细分标签,用于详细分析建筑垃圾.
- 促进深度学习模型的开发和验证,以量化建筑垃圾.
主要方法:
- 利用谷歌地球和GF-2卫星图像作为数据来源.
- 从北京的建筑垃圾填埋场收集和处理了3,653个图像样本.
- 生成的面具标记图像以像素级分类为四类:背景,空置垃圾填埋区,工程设施区和垃圾倾倒区.
主要成果:
- 开发了一个数据集,包含3,653个原始图像样本和相应的面具标记图像.
- 将像素分为四个类别,其中很大一部分用于建筑垃圾和工程设施.
- 为量化建筑垃圾产量提供了详细的像素级语义细分标签.
结论:
- 构建的数据集是建筑垃圾管理学术和工业研究的宝贵资源.
- 能够开发先进的深度学习模型,用于准确的建筑垃圾提取和产量估计.
- 有助于更好的环境监测和可持续城市发展实践.
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