TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
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arXiv (2025)
13 Pith papers cite this work. Polarity classification is still indexing.
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2026 13representative citing papers
UNIGEOCLIP creates a unified embedding for aerial imagery, street views, elevation, text, and coordinates via all-to-all contrastive alignment plus a scaled lat-long encoder, outperforming single-modality and coordinate baselines on geospatial tasks.
A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware evaluation.
A proxy consistency loss trains location encoders on proxy geographic data to outperform direct input fusion or frozen embeddings for air quality and poverty mapping with sparse labels.
EFDiff conditions a diffusion model with Prithvi-EO-2.0 geospatial embeddings via cross-attention to achieve 32x LST super-resolution, outperforming baselines on a global Landsat dataset.
A visual analytics workbench enables scientists to explore, query, and verify embedding-based similarity searches on weather and climate data by tracing results back to physical evidence.
neuroGravity reconstructs transferable human mobility networks from basic urban data via physics-informed deep learning, with transferability predicted by a spatial income segregation index.
A prompting-based adaptation technique lets RGB-trained LMMs process multi-spectral inputs and deliver strong zero-shot gains on remote-sensing benchmarks.
SSDM decouples global geospatial embeddings into structural modulation and semantic injection pathways to improve accuracy and consistency in high-resolution remote sensing land cover mapping.
HuiYanEarth-SAR is a foundation model that generates realistic global SAR imagery from geographic coordinates alone by integrating geospatial semantics and implicit scattering characteristics.
LIANet encodes multi-temporal Earth observation data into a coordinate-based neural field that supports label-only fine-tuning for downstream tasks without access to raw imagery.
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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TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
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UNIGEOCLIP: Unified Geospatial Contrastive Learning
UNIGEOCLIP creates a unified embedding for aerial imagery, street views, elevation, text, and coordinates via all-to-all contrastive alignment plus a scaled lat-long encoder, outperforming single-modality and coordinate baselines on geospatial tasks.
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Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware evaluation.
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A Proxy Consistency Loss for Grounded Fusion of Earth Observation and Location Encoders
A proxy consistency loss trains location encoders on proxy geographic data to outperform direct input fusion or frozen embeddings for air quality and poverty mapping with sparse labels.
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When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution
EFDiff conditions a diffusion model with Prithvi-EO-2.0 geospatial embeddings via cross-attention to achieve 32x LST super-resolution, outperforming baselines on a global Landsat dataset.
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Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration
A visual analytics workbench enables scientists to explore, query, and verify embedding-based similarity searches on weather and climate data by tracing results back to physical evidence.
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Transferable Human Mobility Network Reconstruction with neuroGravity
neuroGravity reconstructs transferable human mobility networks from basic urban data via physics-informed deep learning, with transferability predicted by a spatial income segregation index.
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Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning
A prompting-based adaptation technique lets RGB-trained LMMs process multi-spectral inputs and deliver strong zero-shot gains on remote-sensing benchmarks.
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Structure-Semantic Decoupled Modulation of Global Geospatial Embeddings for High-Resolution Remote Sensing Mapping
SSDM decouples global geospatial embeddings into structural modulation and semantic injection pathways to improve accuracy and consistency in high-resolution remote sensing land cover mapping.
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HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation
HuiYanEarth-SAR is a foundation model that generates realistic global SAR imagery from geographic coordinates alone by integrating geospatial semantics and implicit scattering characteristics.
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Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
LIANet encodes multi-temporal Earth observation data into a coordinate-based neural field that supports label-only fine-tuning for downstream tasks without access to raw imagery.
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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.