Change detection for aerial imagery involves locating and identifying changes associated with the areas of interest between co-registered bi-temporal or multi-temporal images of a geographical location. Farm ponds are man-made structures belonging to the category of minor irrigation structures used to collect surface run-off water for future irrigation purposes. Detection of farm ponds from aerial imagery and their evolution over time helps in land surveying to analyze the agricultural shifts, policy implementation, seasonal effects and climate changes. In this paper, we introduce a publicly available object detection and instance segmentation (OD/IS) dataset for localizing farm ponds from aerial imagery. We also collected and annotated the bi-temporal data over a time-span of 14 years across 17 villages, resulting in a binary change detection dataset called \textbf{F}arm \textbf{P}ond \textbf{C}hange \textbf{D}etection Dataset (\textbf{FPCD}). We have benchmarked and analyzed the performance of various object detection and instance segmentation methods on our OD/IS dataset and the change detection methods over the FPCD dataset. The datasets are publicly accessible at this page: \textit{\url{https://huggingface.co/datasets/ctundia/FPCD}}

翻译：航空影像的变化检测涉及定位和识别同一地理区域配准的双时相或多时相影像中与感兴趣区域相关的变化。农田池塘属于小型灌溉设施，用于收集地表径流水以备未来灌溉之需。从航空影像中检测农田池塘及其随时间演变的趋势，有助于土地勘测，以分析农业转型、政策实施、季节性影响和气候变化。本文介绍了一个公开可用的物体检测与实例分割（OD/IS）数据集，用于从航空影像中定位农田池塘。我们还收集并标注了跨越14年、覆盖17个村庄的双时相数据，由此构建了一个二元变化检测数据集，命名为**农**田**池**塘**变**化**检**测数据集（FPCD）。我们基于OD/IS数据集对多种物体检测与实例分割方法的性能进行了基准测试与分析，并基于FPCD数据集评估了变化检测方法。数据集可通过以下链接公开获取：\textit{\url{https://huggingface.co/datasets/ctundia/FPCD}}