Paper Citation Record · LEDGER

On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2304.06798.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2304.06798 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:06:22.265275Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-23T23:55:53.634329Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 887dd25a-8703-482a-94cc-2cec2483779a · inbound

Quantifying Geospatial in the Common Crawl Corpus cites this paper.

Quantifying Geospatial in the Common Crawl Corpus On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:55:53.637305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e02dcbe-f036-433c-87d7-d6ea9631cd27 · inbound

Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models cites this paper.

Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T05:06:22.265275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44de9e16-95a8-4e4b-a04d-32a8c72cecf1 · inbound

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution cites this paper.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T23:04:06.785792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc6e954-332f-431f-9275-363516c91970 · inbound

Scalable Geospatial Data Generation Using AlphaEarth Foundations Model cites this paper.

Scalable Geospatial Data Generation Using AlphaEarth Foundations Model On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T19:53:34.419318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b41188e-fea3-41d5-af1c-c06ccf605dec · inbound

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

Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:33:41.785409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e269a66-e4a4-40f3-b713-5abadf3d060e · inbound

ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding cites this paper.

ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:06:09.837942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7da27878-ff2c-481f-b5dc-96c6867f8e73 · inbound

Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents cites this paper.

Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 87

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:31:12.122154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 56cea514-34ab-44a9-ac46-a9f95d4e1717 · 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 On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:31.636704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb016fec-6d09-4cfb-8a66-b555952d77da · inbound

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

Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:05:02.468686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.