  Deep learning has led to many recent advances in object detection and
instance segmentation, among other computer vision tasks. These advancements
have led to wide application of deep learning based methods and related
methodologies in object detection tasks for satellite imagery. In this paper,
we introduce MIS Check-Dam, a new dataset of check-dams from satellite imagery
for building an automated system for the detection and mapping of check-dams,
focusing on the importance of irrigation structures used for agriculture. We
review some of the most recent object detection and instance segmentation
methods and assess their performance on our new dataset. We evaluate several
single stage, two-stage and attention based methods under various network
configurations and backbone architectures. The dataset and the pre-trained
models are available at https://www.cse.iitb.ac.in/gramdrishti/.
