Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation

arXiv:2609.37481 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Adaptation — 26× in this paper · explained in 1 episode
• Accuracy — 24× in this paper · explained in 1 episode
• Core — 20× in this paper · explained in 2 episodes
• Consistency — 18× in this paper · explained in 3 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• Foundation Model — 15× in this paper · explained in 3 episodes
• Distillation — 13× in this paper · explained in 2 episodes
• Regularization — 9× in this paper · explained in 3 episodes
• Active Learning — 8× in this paper · explained in 6 episodes
• Pseudo-labeling — 8× in this paper · explained in 3 episodes
• Knowledge Distillation — 7× in this paper · explained in 11 episodes
• Semi-supervised Learning — 7× in this paper · explained in 4 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Pseudo-labels — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Over-smoothing — 2× in this paper · explained in 4 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Strategy — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Energy Efficiency — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Enabling Near-realtime Remote Sensing via Satellite-Ground Collaboration of Large Vision-Language Models
• Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
• Distilling the Knowledge in a Neural Network
• FitNets: An Adaptive Framework to Learn Accurate Traffic Distributions

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