Empirical study finds synthetic-to-real domain gap sharply degrades diffusion SR models on real cross-sensor satellite pairs while real-data training faces optimization and adaptation problems.
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EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.
GHGbench supplies a harmonized dataset and multi-task benchmark for company and building carbon emission prediction, with baselines showing large OOD gaps and benefits from multimodal embeddings.
DeluluNet enables continued prediction under modality substitution, addition, or subsets by training a multi-modal model from a unimodal teacher and unlabeled multimodal data via modality hallucination.
A two-step framework combines stacked hurdle random forest models for local severity prediction with semi-parametric spatio-temporal modeling to reconstruct large-scale disease dynamics from imperfect indicators, demonstrated on sugar beet yellows in France.
FireDataForge automates retrieval and harmonization of 11 multi-source wildfire geospatial datasets into common-grid NumPy arrays for a given MTBS Event ID.
VibrantForests produces coherent 10m wall-to-wall estimates of multiple forest structure attributes across the US by applying satellite models trained on lidar samples.
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
citing papers explorer
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Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution
Empirical study finds synthetic-to-real domain gap sharply degrades diffusion SR models on real cross-sensor satellite pairs while real-data training faces optimization and adaptation problems.
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Expected Free Energy-based Planning as Variational Inference
EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
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What Type of Inference is Active Inference?
EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.
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GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction
GHGbench supplies a harmonized dataset and multi-task benchmark for company and building carbon emission prediction, with baselines showing large OOD gaps and benefits from multimodal embeddings.
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Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors
DeluluNet enables continued prediction under modality substitution, addition, or subsets by training a multi-modal model from a unimodal teacher and unlabeled multimodal data via modality hallucination.
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Predicting disease severity and large-scale spread from coupled severity measurements and imperfect indicators: Application to beet yellows
A two-step framework combines stacked hurdle random forest models for local severity prediction with semi-parametric spatio-temporal modeling to reconstruct large-scale disease dynamics from imperfect indicators, demonstrated on sugar beet yellows in France.
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FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration
FireDataForge automates retrieval and harmonization of 11 multi-source wildfire geospatial datasets into common-grid NumPy arrays for a given MTBS Event ID.
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Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
VibrantForests produces coherent 10m wall-to-wall estimates of multiple forest structure attributes across the US by applying satellite models trained on lidar samples.
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Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
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Earth Embeddings Reveal Diverse Urban Signals from Space
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.