城市树种基准数据集用于时间序列分类
Clément Bressant1, Romain Wenger1, David Michéa2
1LIVE UMR 7362 CNRS, University of Strasbourg, 3 rue de l'Argonne, Strasbourg, 67000, France.
Data in brief
|July 4, 2025
概括
一个新的数据集可以使用卫星图像和深度学习来对城市树种进行分类. 这有助于推进城市植被监测和气候弹性战略.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 计算机科学 计算机科学
背景情况:
- 城市树木分类对于生态理解和气候适应性至关重要.
- 卫星图像时间序列 (SITS) 为城市植被监测提供了一个有希望的方法.
研究的目的:
- 通过多源SITS引入城市树种分类的基准数据集.
- 为评估深度学习模型和城市植被分析的融合策略提供可复制的框架.
主要方法:
- 通过使用Sentinel-2和PlanetScope对法国斯特拉斯堡的图像创建了一个数据集.
- 它包括20个物种的45,084棵树,格式为时间序列分类.
- 培训和评估了三个基于InceptionTime的深度学习模型.
主要成果:
- 该数据集支持直接集成到深度学习框架中.
- 训练有素的模型实现了精确的物种分类,输出包括信心得分和正确性标志.
- 交互式t-SNE可视化有助于解释性和错误分析.
结论:
- 提出的数据集和框架提升了城市植被监测能力.
- 这项工作有助于开发以自然为基础的城市气候弹性解决方案.
- 它为遥感,城市生态和机器学习领域的研究人员提供了宝贵的资源.
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