  Remote sensing data is crucial for applications ranging from monitoring
forest fires and deforestation to tracking urbanization. Most of these tasks
require dense pixel-level annotations for the model to parse visual information
from limited labeled data available for these satellite images. Due to the
dearth of high-quality labeled training data in this domain, there is a need to
focus on semi-supervised techniques. These techniques generate pseudo-labels
from a small set of labeled examples which are used to augment the labeled
training set. This makes it necessary to have a highly representative and
diverse labeled training set. Therefore, we propose to use an active
learning-based sampling strategy to select a highly representative set of
labeled training data. We demonstrate our proposed method's effectiveness on
two existing semantic segmentation datasets containing satellite images: UC
Merced Land Use Classification Dataset and DeepGlobe Land Cover Classification
Dataset. We report a 27% improvement in mIoU with as little as 2% labeled data
using active learning sampling strategies over randomly sampling the small set
of labeled training data.
