Pith. sign in

Paper Citation Record · LEDGER

OpenStreetView-5M: The Many Roads to Global Visual Geolocation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.18873.

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

pith.paper-citation-record.v1
2404.18873 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:52:09.120531Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:44:48.674617Z

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 9ad4f2a6-8671-4596-87c9-e9dc77b2b336 · inbound

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models cites this paper.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models OpenStreetView-5M: The Many Roads to Global Visual Geolocation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:09.120531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:09.120531Z digest=sha256:92d6551791310613abf4f0ae9ddc854d8fe264b9a03e2cf3f249f601c57dc55e

Observation fce76d2a-e0d6-4691-badb-072bdf50e4ad · inbound

Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation cites this paper.

Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation OpenStreetView-5M: The Many Roads to Global Visual Geolocation

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-05T12:44:48.742246Z

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.

source=pdf_text observed=2026-08-05T12:44:41.969293Z digest=sha256:bc220461727ae775b5887b01118797c8e61cb6fe116a16ac240c1d9516596bdd