2014年美国加州埃尔塞贡多市土地覆盖分类样本数据

本数据为2014年美国加州埃尔塞贡多市部分区域的土地覆盖分类样本，包括含有土地覆盖分类类型的矢量样本点，以及与样本点匹配的样本影像。土地覆盖类别参照美国农业部拍摄的DOQQ（Digital Orthophoto Quarer Quad）影像通过目视解译进行标注，其中丘拉维斯塔市样本分为以下7类：建筑、停车区、道路、裸土、阴影、树木和水体。埃尔塞贡多样本分为以下7类：建筑、停车区、道路、裸土、阴影、树木和其他植被。本数据集可直接使用GIS软件开并进行后续处理，并可用于机器学习和深度学习土地覆盖分类模型的开发。

This dataset contains land cover classification samples from selected areas in El Segundo, California, USA, dating back to 2014. It includes vector sample points with pre-defined land cover category labels and corresponding matching imagery. All category labels were annotated through visual interpretation based on DOQQ (Digital Orthophoto Quarter Quad) imagery captured by the United States Department of Agriculture (USDA). Samples from Chula Vista are divided into 7 categories: buildings, parking areas, roads, bare soil, shadows, trees, and water bodies. Samples from El Segundo fall into 7 classes: buildings, parking areas, roads, bare soil, shadows, trees, and other vegetation. This dataset can be directly used with GIS software for further processing, and can be employed to develop machine learning and deep learning-based land cover classification models.