GeoNLI - A Natural Language Interpreter for Satellite Imagery

arXiv:2609.28741 · cs.CV · Submitted 2026-09-23 · Read on arXiv

cs.CV

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Grounding — 52× in this paper · explained in 3 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Visual Grounding — 10× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Segmentation Masks — 3× in this paper · explained in 2 episodes
• Semantic Understanding — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Guided attention — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Visual Question Answering — 2× in this paper
• Aggregation — 1× in this paper · explained in 1 episode
• Aperture — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Synthetic Aperture Radar — 1× in this paper
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
• Segment Anything
• SAM 2: Segment Anything in Images and Videos
• SAM 3: Segment Anything with Concepts
• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
• Falcon: A Remote Sensing Vision-Language Foundation Model (Technical Report)
• EarthMind: Leveraging Cross-Sensor Data for Advanced Earth Observation Interpretation with a Unified Multimodal LLM
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• Accelerating Diffusion for SAR-to-Optical Image Translation via Adversarial Consistency Distillation

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