Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
A key challenge for much of the machine learning work on remote sensing and
earth observation data is the difficulty in acquiring large amounts of
accurately labeled data. This is particularly true for semantic segmentation
tasks, which are much less common in the remote sensing domain because of the
incredible difficulty in collecting precise, accurate, pixel-level annotations
at scale. Recent efforts have addressed these challenges both through the
creation of supervised datasets as well as the application of self-supervised
methods. We continue these efforts on both fronts. First, we generate and
release an improved version of the Agriculture-Vision dataset (Chiu et al.,
2020b) to include raw, full-field imagery for greater experimental flexibility.
Second, we extend this dataset with the release of 3600 large, high-resolution
(10cm/pixel), full-field, red-green-blue and near-infrared images for
pre-training. Third, we incorporate the Pixel-to-Propagation Module Xie et al.
(2021b) originally built on the SimCLR framework into the framework of MoCo-V2
Chen et al.(2020b). Finally, we demonstrate the usefulness of this data by
benchmarking different contrastive learning approaches on both downstream
classification and semantic segmentation tasks. We explore both CNN and Swin
Transformer Liu et al. (2021a) architectures within different frameworks based
on MoCo-V2. Together, these approaches enable us to better detect key
agricultural patterns of interest across a field from aerial imagery so that
farmers may be alerted to problematic areas in a timely fashion to inform their
management decisions. Furthermore, the release of these datasets will support
numerous avenues of research for computer vision in remote sensing for
agriculture.