ChangeQuery is a new multimodal framework for semantic disaster change analysis that combines optical and SAR data with a custom dataset and annotation pipeline to support interactive damage assessment.
On the opportunities and challenges of foundation models for geospatial artificial intelligence
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
verdicts
UNVERDICTED 6representative citing papers
A fleet of sensor-specialized 22M-parameter JEPA models routed by an LLM improves LLM-as-judge scores on hydrologic questions over AlphaEarth alone with Cohen's d of 1.10.
AlphaEarth embeddings form a rotating 13-dimensional manifold where local geometry predicts retrieval quality, and an agentic system using nine geometric tools outperforms parametric reasoning on environmental queries.
Analysis estimates 18.7% of Common Crawl documents contain geospatial information like coordinates and addresses, with little difference by language.
The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.
Foundation model embeddings provide no advantage over traditional spectral features for cross-country maize yield generalization in Africa, with all methods yielding negative R² under leave-one-country-out testing due to distribution shifts.
citing papers explorer
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ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding
ChangeQuery is a new multimodal framework for semantic disaster change analysis that combines optical and SAR data with a custom dataset and annotation pipeline to support interactive damage assessment.
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Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence
A fleet of sensor-specialized 22M-parameter JEPA models routed by an LLM improves LLM-as-judge scores on hydrologic questions over AlphaEarth alone with Cohen's d of 1.10.
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Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning
AlphaEarth embeddings form a rotating 13-dimensional manifold where local geometry predicts retrieval quality, and an agentic system using nine geometric tools outperforms parametric reasoning on environmental queries.
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Quantifying Geospatial in the Common Crawl Corpus
Analysis estimates 18.7% of Common Crawl documents contain geospatial information like coordinates and addresses, with little difference by language.
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Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents
The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.
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Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa
Foundation model embeddings provide no advantage over traditional spectral features for cross-country maize yield generalization in Africa, with all methods yielding negative R² under leave-one-country-out testing due to distribution shifts.