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Remote sensing data is crucial for applications ranging from monitoringforest fires and deforestation to tracking urbanization. Most of these tasksrequire dense pixel-level annotations for the model to parse visual informationfrom limited labeled data available for these satellite images. Due to thedearth of high-quality labeled training data in this domain, there is a need tofocus on semi-supervised techniques. These techniques generate pseudo-labelsfrom a small set of labeled examples which are used to augment the labeledtraining set. This makes it necessary to have a highly representative anddiverse labeled training set. Therefore, we propose to use an activelearning-based sampling strategy to select a highly representative set oflabeled training data. We demonstrate our proposed method's effectiveness ontwo existing semantic segmentation datasets containing satellite images: UCMerced Land Use Classification Dataset and DeepGlobe Land Cover ClassificationDataset. We report a 27% improvement in mIoU with as little as 2% labeled datausing active learning sampling strategies over randomly sampling the small setof labeled training data.

Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images