# Dynamic Class-Aware Active Learning for Unbiased Satellite Image Segmentation Source: [Dynamic Class-Aware Active Learning for Unbiased Satellite Image Segmentation]() Collection index: [llms.txt]() Data updated: 2026-04-14T05:00:04.234Z Authors: Gadi Hemanth Kumar, Athira Nambiar, Pankaj Bodani Published: 2026-04-10T05:08:57.000Z ## Abstract Semantic segmentation of satellite imagery plays a vital role in land cover mapping and environmental monitoring. However, annotating large-scale, high-resolution satellite datasets is costly and time consuming, especially when covering vast geographic regions. Instead of randomly labeling data or exhaustively annotating entire datasets, Active Learning (AL) offers an efficient alternative by intelligently selecting the most informative samples for annotation with the help of Human-in-the-loop (HITL), thereby reducing labeling costs while maintaining high model performance. AL is particularly beneficial for large-scale or resource-constrained satellite applications, as it enables high segmentation accuracy with significantly fewer labeled samples. Despite these advantages, standard AL strategies typically rely on global uncertainty or diversity measures and lack the adaptability to target underperforming or rare classes as training progresses, leading to bias in the system. To overcome these limitations, we propose a novel adaptive acquisition function, Dynamic Class-Aware Uncertainty based Active learning (DCAU-AL) that prioritizes sample selection based on real-time class-wise performance gaps, thereby overcoming class-imbalance issue. The proposed DCAU-AL mechanism continuously tracks the performance of the segmentation per class and dynamically adjusts the sampling weights to focus on poorly performing or underrepresented classes throughout the active learning process. Extensive experiments on the OpenEarth land cover dataset show that DCAU-AL significantly outperforms existing AL methods, especially under severe class imbalance, delivering superior per-class IoU and improved annotation efficiency. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Satellite Segmentation on OpenEarth (test)](): Satellite Segmentation; OpenEarth (test); mIoU: 66.4. - [Semantic Segmentation on OpenEarth (test)](): Semantic segmentation; OpenEarth (test); mIoU: 0.664. ## Original sources and implementation [Original paper]() [PDF]()