SkyWCD地表水体精细分类遥感样本数据集

本数据集面向遥感图像中各种水体类型的自动提取。
解决的主要问题：
地表水体作为一种非常典型的地物要素，受地表水体的面积、地形条件、降雨量、经济发展水平、水资源利用方式等各类因素影响，造成难以根据水体特征和遥感影像特征进行分类，这是目前遥感自动分类精度低的主要原因。人工目视分类可以有效识别水利类型，但由于水体数据量巨大，单纯依靠人工目视分类不可行。本数据集主要解决目前缺少可以训练智能模型的水体类型样本问题。
有益效果：
结合目前地表水体类型以及遥感影像特征，对地表水体进行重新分类，并利用高分辨率遥感影像提取各类水体的样本图片，从而可以为训练地表水体智能模型提供训练数据，能大大提高大量地表水体的分类精度和效率。

This dataset is intended for the automatic extraction of various water body types from remote sensing images.
Key Addressed Problems:
As a highly typical surface feature, surface water bodies are influenced by multiple factors including their area, topographic conditions, rainfall, economic development level, and water resource utilization patterns. This renders it challenging to classify surface water bodies based on their own characteristics and remote sensing image features, which is the primary cause of the low accuracy of current remote sensing automatic classification. Manual visual classification can effectively identify water body types, but it is infeasible to rely solely on manual visual classification due to the massive volume of water body data. This dataset mainly addresses the current shortage of water body type samples available for training intelligent models.
Beneficial Effects:
By integrating existing surface water body classification systems and remote sensing image features, we reclassify surface water bodies and extract sample images of various water types using high-resolution remote sensing images. This can provide training data for intelligent models for surface water body extraction, significantly improving the classification accuracy and efficiency of large-scale surface water bodies.