Active Label Refinement for Semantic Segmentation of Satellite Images
Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation networks are typically trained on large sets of labelled satellite images. However, obtaining expert labels for these images is costly. Therefore, we propose to rely on a low-cost approach, e.g. crowdsourcing or pretrained networks, to label the images in the first step. Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step. We evaluate the active learning strategies using satellite images of Bengaluru in India, labelled with land cover and land use labels. Our experimental results suggest that an active label refinement to improve the semantic segmentation network's performance is beneficial.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement
Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood…
Domain AdaptationSegmentationSemantic SegmentationActive Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images
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 inf…
Active LearningLand Cover ClassificationSemantic SegmentationSemi-Supervised Semantic SegmentationSemantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning an…
SegmentationSemantic SegmentationZero-Shot Semantic SegmentationDynamic Class-Aware Active Learning for Unbiased Satellite Image Segmentation
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, especial…
Semantic SegmentationImage SegmentationActive LearningEvaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery
Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the ad…
Data AugmentationSegmentationSemantic Segmentation