COP-GEN models multimodal Copernicus Earth observation data as conditional distributions via a latent diffusion transformer, producing diverse physically consistent outputs and covering 90% of the real observation manifold on a new stochastic benchmark.
Olmoearth: Stable latent image model- ing for multimodal earth observation
6 Pith papers cite this work. Polarity classification is still indexing.
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Introduces the SMART-HC-VQA dataset with 65k single-image and 2.3M temporal VQA examples plus an adapted LLaVA-NeXT MLLM framework for geospatial-temporal sensemaking of remote sensing construction activity.
A generative compression model using historical priors for Earth observation data achieves up to 10,000x reduction after exascale training on an Armv9 supercomputer.
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
Embedding-only uplink enables flexible onboard retrieval for remote sensing under distribution shifts, with kNN superior for cloud classification and centroids for temporal change detection.
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.
citing papers explorer
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COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data
COP-GEN models multimodal Copernicus Earth observation data as conditional distributions via a latent diffusion transformer, producing diverse physically consistent outputs and covering 90% of the real observation manifold on a new stochastic benchmark.
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Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model
Introduces the SMART-HC-VQA dataset with 65k single-image and 2.3M temporal VQA examples plus an adapted LLaVA-NeXT MLLM framework for geospatial-temporal sensemaking of remote sensing construction activity.
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Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
A generative compression model using historical priors for Earth observation data achieves up to 10,000x reduction after exascale training on an Armv9 supercomputer.
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Heterogeneous Scientific Foundation Model Collaboration
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
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Embedding-Only Uplink for Onboard Retrieval Under Shift in Remote Sensing
Embedding-only uplink enables flexible onboard retrieval for remote sensing under distribution shifts, with kNN superior for cloud classification and centroids for temporal change detection.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.