  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}}
