A Composition-Aware Pretraining Framework for Geospatial Foundation Models

arXiv:2608.30817 · cs.CV, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Terminology

• Foundation Model — 33× in this paper · explained in 3 episodes
• Embeddings — 25× in this paper · explained in 2 episodes
• Foundation Models — 18× in this paper · explained in 7 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Clustering — 9× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Distillation — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Aerial Image Dataset — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Multi-Layer Perceptron — 2× in this paper
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Token Routing and Incremental Modeling — 1× in this paper
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Bidirectional Multi-Instance Learning — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Foundation Models (FMs) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• LEVIR Change Detection — 1× in this paper
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• State Space Model — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models
• ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• DINOv3
• AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification

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