Paper Citation Record · LEDGER

GeoLLM: Extracting Geospatial Knowledge from Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2310.06213.

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

pith.paper-citation-record.v1
2310.06213 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:42:13.797027Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:07:25.922043Z

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 d5331e87-13c2-4e5f-b2bd-c84b6b5d71f5 · inbound

General Geospatial Inference with a Population Dynamics Foundation Model cites this paper.

General Geospatial Inference with a Population Dynamics Foundation Model GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:33:16.154127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d0dc250c-5d54-48ad-b17e-e1944aea188f · inbound

POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning cites this paper.

POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T19:42:13.797027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d734da45-69a4-4073-9f19-4093b720d1d3 · 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 GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:12.402866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 022f91d7-e753-4866-b3f3-4ec997b03d13 · inbound

MapStory: Prototyping Editable Map Animations with LLM Agents cites this paper.

MapStory: Prototyping Editable Map Animations with LLM Agents GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:22:05.010468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d35b7c6b-558e-48b2-9c0c-91612e231d3a · inbound

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features cites this paper.

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:14:02.784499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 490394fa-ac71-44ab-b088-4000bea27e41 · inbound

Around the World in 24 Hours: Probing LLM Knowledge of Time and Place cites this paper.

Around the World in 24 Hours: Probing LLM Knowledge of Time and Place GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:55:00.713267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a47c787-05ef-4e25-8499-bbdf2ea77e2f · inbound

A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications cites this paper.

A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 165

Resolution: unresolved; no resolver link, observed 2026-08-07T00:48:23.029769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bd645c0-cb67-4a04-a6ae-bdb8b142b805 · inbound

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning cites this paper.

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T14:20:39.492659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6128adf1-c526-4a15-9008-89ebbe6abf2a · inbound

SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces cites this paper.

SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:35:18.788560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d1195bf-6f15-4363-a93e-5e2fc6d600c5 · inbound

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand cites this paper.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:48:15.232739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12eb8677-eb9c-4bd2-b6f0-8929d9e7faa7 · inbound

Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility cites this paper.

Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:16:25.182438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3abbf723-d690-4fe8-97e5-cd25ca98f0ee · inbound

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models cites this paper.

From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:26:45.805544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a3dcfa8-5a79-488e-8e98-85a0e0d3921d · inbound

GIScholarBench: Benchmarking LLM Overconfidence in GIS Research cites this paper.

GIScholarBench: Benchmarking LLM Overconfidence in GIS Research GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:07:25.923699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.