Effective water resource management is crucial in agricultural regions like northeastern Thailand, where limited water retention in sandy soils poses significant challenges. In response to this issue, the Aerial Image Water Resource (AIWR) dataset was developed, comprising 800 aerial images focused on natural and artificial water bodies in this region. The dataset was created using Bing Maps and follows the standards of the Fundamental Geographic Data Set (FGDS). It includes ground truth annotations validated by experts in remote sensing, making it an invaluable resource for researchers in geoinformatics, computer vision, and artificial intelligence. The AIWR dataset presents considerable challenges, such as segmentation due to variations in the size, color, shape, and similarity of water bodies, which often resemble other land use categories. The objective of the proposed dataset is to explore advanced AI-driven methods for water body segmentation, addressing the unique challenges posed by the dataset complexity and limited size. This dataset and related research contribute to the development of novel algorithms for water management, supporting sustainable agricultural practices in regions facing similar challenges.

翻译：有效的水资源管理对于泰国东北部等农业区域至关重要，该地区沙质土壤的有限保水能力带来了重大挑战。针对这一问题，本研究开发了航拍图像水资源（AIWR）数据集，包含800张聚焦于该区域自然与人工水体的航拍图像。该数据集基于Bing Maps构建，遵循基础地理数据集（FGDS）标准，并包含经遥感专家验证的真实标注，为地理信息学、计算机视觉和人工智能领域的研究者提供了宝贵资源。AIWR数据集提出了显著挑战，例如因水体尺寸、颜色、形状的差异及其与其他土地利用类别的相似性而导致的分割困难。本数据集旨在探索先进的人工智能驱动的水体分割方法，以应对数据集复杂性和有限规模带来的独特挑战。该数据集及相关研究有助于开发新型水资源管理算法，为面临类似挑战地区的可持续农业实践提供支持。