Foundation Models for Generalist Geospatial Artificial Intelligence | Paper Citation Record

Observation e9d028f5-0d82-4a69-9358-b8b8468a1c8c · inbound

Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels cites this paper.

Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:58:34.371146Z

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Transforming the Hybrid Cloud for Emerging AI Workloads cites this paper.

Transforming the Hybrid Cloud for Emerging AI Workloads Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 154

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SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery cites this paper.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 2

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WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks cites this paper.

WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 20

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Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space cites this paper.

Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 7

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Observation ea4955a1-6e76-48c5-9ae0-882d78614e53 · inbound

Improving Satellite Imagery Masking using Multi-task and Transfer Learning cites this paper.

Improving Satellite Imagery Masking using Multi-task and Transfer Learning Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 43

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AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities cites this paper.

AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 38

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WildSAT: Learning Satellite Image Representations from Wildlife Observations cites this paper.

WildSAT: Learning Satellite Image Representations from Wildlife Observations Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 32

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Observation 47d2a8e9-985b-496c-85d0-bd2295a702af · inbound

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product cites this paper.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 84

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Observation a4b3cb9c-afa1-4784-a0f9-d8ad3b67f213 · inbound

How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models? cites this paper.

How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models? Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 13

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Observation 1ee0a183-e67a-4cfc-926a-0cbea94d3141 · inbound

GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations cites this paper.

GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T22:42:13.374984Z

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Observation c6179353-a5ab-4376-a2e8-b5e7e76d7be5 · inbound

Dargana: fine-tuning EarthPT for dynamic tree canopy mapping from space cites this paper.

Dargana: fine-tuning EarthPT for dynamic tree canopy mapping from space Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 2023

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Observation 98b9e70f-e30a-47f6-b972-4de37eb3b5ec · inbound

Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models cites this paper.

Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 21

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PyViT-FUSE: A Foundation Model for Multi-Sensor Earth Observation Data cites this paper.

PyViT-FUSE: A Foundation Model for Multi-Sensor Earth Observation Data Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 7

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Observation 6c07c81a-f26b-424d-950a-b56dda2d4d9c · inbound

A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning cites this paper.

A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 16

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Observation 7f5ff28e-64a0-4874-a090-9a82d904d9a0 · inbound

IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping cites this paper.

IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 26

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ExEBench: Benchmarking Foundation Models on Extreme Earth Events cites this paper.

ExEBench: Benchmarking Foundation Models on Extreme Earth Events Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 38

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Observation fc78dbde-2c4c-4696-a112-9268c208b36a · inbound

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks cites this paper.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:52.735664Z

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Observation 6b6a96c9-2bf0-469c-be63-d1f17b0a286b · inbound

Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification cites this paper.

Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 19

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Observation d59b86af-9fe3-4c19-916d-4ae7ca69bfc0 · inbound

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations cites this paper.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 55

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VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond cites this paper.

VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 44

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Observation 9ff4e5bd-1fe4-4fa8-a74b-88bfeb01d695 · inbound

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor cites this paper.

Defining Foundation Models for Computational Science: A Call for Clarity and Rigor Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

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Geospatial Foundation Models to Enable Progress on Sustainable Development Goals cites this paper.

Geospatial Foundation Models to Enable Progress on Sustainable Development Goals Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 100

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CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities cites this paper.

CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

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Observation 36b62bad-0791-4bff-8c47-8f04b0a4cba8 · inbound

Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning cites this paper.

Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 22

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SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images cites this paper.

SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 20

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Observation 12e18c33-8afc-499d-8ce1-6709760046c0 · inbound

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data cites this paper.

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

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Observation 0713186c-cc79-4047-a01f-3e84e026be52 · inbound

Surya: Foundation Model for Heliophysics cites this paper.

Surya: Foundation Model for Heliophysics Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 36

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Observation 7f681572-d034-45af-891e-5fff53206ed3 · inbound

Trees as Gaussians: Large-Scale Individual Tree Mapping cites this paper.

Trees as Gaussians: Large-Scale Individual Tree Mapping Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 29

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Observation dd5a5098-80de-4de1-b68d-43bcb94c530a · inbound

Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves cites this paper.

Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 36

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Observation f27e7f67-564c-49b7-9fc5-c78dedb409ee · inbound

GeoAnalystBench: A GeoAI benchmark for assessing large language models for spatial analysis workflow and code generation cites this paper.

GeoAnalystBench: A GeoAI benchmark for assessing large language models for spatial analysis workflow and code generation Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 15

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Observation 665d7f49-a1b6-4ccc-af74-d4ad97631816 · inbound

An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia cites this paper.

An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 23

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Observation 1a5fc2ae-3c63-42cf-938a-21234141598e · inbound

LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels cites this paper.

LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 75

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The View From Space: Navigating Instrumentation Differences with EOFMs cites this paper.

The View From Space: Navigating Instrumentation Differences with EOFMs Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 7

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UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations cites this paper.

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 34

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Observation bdf60dd4-2368-40f1-9131-05fc4285ab3b · inbound

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation cites this paper.

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:22:09.042099Z

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Observation 1f1f1586-dcf9-4ca7-aa6f-48dfd1659018 · inbound

How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models cites this paper.

How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 2018

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Observation f46c4b3c-20bd-4e28-bd6d-6e3d7f3ab712 · inbound

OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents cites this paper.

OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T22:10:49.766293Z

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Observation 3ab36206-251c-4ee6-8371-ac7351f4c544 · inbound

Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data cites this paper.

Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:20:52.331032Z

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Observation 28b3ba9d-a82a-49e8-9bd7-6c10d93c11ba · inbound

Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning cites this paper.

Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:33:41.815679Z

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Observation 73ff8dc8-2805-4f26-a699-b7abf62c52f3 · inbound

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning cites this paper.

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:24:47.562225Z

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Observation 297a5344-b488-4971-ba30-0be29770419c · inbound

Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance cites this paper.

Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:24:47.472838Z

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Observation c28f4d8f-f9f2-40de-91e7-29087e0bb253 · inbound

Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data cites this paper.

Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:31:21.894769Z

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Observation 812ce10f-48af-4c50-bac1-e12f14d1217f · inbound

Rethinking Electro-Optical Vision Foundation Models for Remote Sensing Retrieval: A Controlled Comparison with Generalist VFM cites this paper.

Rethinking Electro-Optical Vision Foundation Models for Remote Sensing Retrieval: A Controlled Comparison with Generalist VFM Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:08.615615Z

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Observation 3dc5bbae-b994-45fd-869f-6851e73d91f6 · inbound

Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa cites this paper.

Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:56:31.692346Z

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Observation fd319c25-0c98-4e73-b190-531d7d6d9c2b · inbound

WATCH: Wide-Area Archaeological Site Tracking for Change Detection cites this paper.

WATCH: Wide-Area Archaeological Site Tracking for Change Detection Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:01:28.620521Z

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Observation b4f7fac7-f50e-467d-a21b-4d92703eb84c · inbound

Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence cites this paper.

Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:05:02.462667Z

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Observation 4f4411c3-c2e0-4868-bb8a-43da7eb114d7 · inbound

SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining cites this paper.

SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:29:39.734896Z

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Observation 0b0d5e0d-433a-47bb-bf26-058a7a1ec97f · inbound

Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing cites this paper.

Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T08:03:13.376684Z

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Observation bed4b5cd-c652-4463-93d0-0a97f974b0be · inbound

Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events cites this paper.

Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:41:18.397400Z

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Observation 63c35809-64dd-4580-9310-2ae7db58faab · inbound

Adapting Prithvi-EO for Fallow Detection for Food-Water Nexus: ViT-Adapter Necks and Parameter-Efficient Backbone tuning of Geospatial Foundation Model cites this paper.

Adapting Prithvi-EO for Fallow Detection for Food-Water Nexus: ViT-Adapter Necks and Parameter-Efficient Backbone tuning of Geospatial Foundation Model Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:18:03.461072Z

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Observation 0b252f86-d626-4958-aa2b-0accb85474de · inbound

Flood Mapping from RGB imagery using a Vision Foundation Model cites this paper.

Flood Mapping from RGB imagery using a Vision Foundation Model Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T15:09:55.413571Z

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Observation b9d9ea71-af09-40e7-b0c3-03b1aed3e674 · inbound

Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones cites this paper.

Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:14:25.390265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bea7ff8c-4f5e-4a0e-b98d-3cc7b6d0ca66 · inbound

Benchmarking Geospatial Foundation Models for Agriculture Applications cites this paper.

Benchmarking Geospatial Foundation Models for Agriculture Applications Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:54:22.253821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4473dda-7be9-4130-8b75-75a643fbc67b · inbound

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring cites this paper.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T04:37:52.101102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8da3d7e4-2d58-43d9-85b9-abdc455234e1 · inbound

Now We Know? A Systematic Comparison of TerraMind and THOR cites this paper.

Now We Know? A Systematic Comparison of TerraMind and THOR Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T15:15:41.136397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc2b091-2400-4298-b75e-e6d3574d6177 · inbound

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets cites this paper.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:23.186753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64e85cb1-5fd9-406c-a889-d0d9ee97518c · inbound

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps cites this paper.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T15:29:40.341874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c03cc83-6a39-44b7-a640-09d69cc19498 · inbound

PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series cites this paper.

PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T15:20:04.265240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfcbe4e0-9447-4675-b00d-7b67ba257990 · inbound

SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models cites this paper.

SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T21:46:33.000441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 948cd988-a088-4a0b-861e-dfe16d13b670 · inbound

Above-ground Biomass Estimation with Geospatial Foundation Models cites this paper.

Above-ground Biomass Estimation with Geospatial Foundation Models Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T16:35:15.878408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5acad382-8b5b-46c9-80b6-50fa0f61f2d5 · inbound

Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality cites this paper.

Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T20:00:29.474456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5974c8d-0253-4155-a751-252de864f38d · inbound

Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality cites this paper.

Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T04:17:30.704049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9043007-02e2-444a-a6ca-314e9004748c · inbound

HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities cites this paper.

HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T00:33:19.928011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference resolution

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