Pith. sign in

Paper Citation Record · LEDGER

Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2503.13502.

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

pith.paper-citation-record.v1
2503.13502 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:16:46.658276Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:22:59.053639Z

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 c42d3bef-8bc9-4754-99a4-2bd5442d0cc2 · inbound

GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization cites this paper.

GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T20:16:46.658276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:16:46.658276Z digest=sha256:784ebbef4cc90ec5f1372aa6732e2c8162fdc8e5c7c17bd80180372e3fa12b0c

Observation 310ef7c7-f971-4d69-9794-0d285f051129 · inbound

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting cites this paper.

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T15:36:48.555055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:36:48.555055Z digest=sha256:aad519f5ff9a075b6772038cb6d21df8b9ba58ecdeec2d019af57973326772cb

Observation 10897631-2eb4-4146-b655-2a6e066fc450 · inbound

From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places cites this paper.

From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T19:54:19.115701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:54:19.115701Z digest=sha256:d4752e02384c3837072b8c10871406bf6f42aa196e1f18ce1c23a6af3a9b01de

Observation 6be8a144-ebf7-424b-bab4-a262aef07469 · inbound

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs cites this paper.

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T23:01:34.577356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:01:34.577356Z digest=sha256:ca5cff587e611a266b3792b8c402207388e1b2e6648f87cae1207348039ffdc2

Observation 199b33ba-b874-464b-832e-45d0ce9e69ce · inbound

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications cites this paper.

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-06T21:07:10.915325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:07:10.915325Z digest=sha256:03aee306995711abc69cdc42e95e6fab2989d3c4d9e4048039365fb494607a7d

Observation 84d3a2ec-cb5c-4134-82ef-843a2f4b39b8 · inbound

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data cites this paper.

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T22:53:14.812865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:53:14.812865Z digest=sha256:fc68f4640ca8cae788c6121243fc1dff00df13d8f450b787e056b0be3f93e94b

Observation 9af14fc2-0088-41df-89fe-c5feae07aee2 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:21:25.976357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-12T03:27:14.464987Z digest=sha256:f063d8497c2118cf634c507f1686f37bd5385cf5d5811a829c23ad17e3e216c5

Observation e89438a8-2cc6-4f11-8f74-0380ac6d23a8 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:22:59.055621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-14T21:21:32.256476Z digest=sha256:58abcb22ef2e0cf3afe37aae4194379dc30cdb1ba1da7d8a3836d7c6ad65bd78