Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.15199.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:43:56.023454Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T10:32:46.419459Z
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 1521ee6f-1fff-4c6d-968e-da9bcee52f24 · inbound
CLDTracker: A Comprehensive Language Description for Visual Tracking cites this paper.
CLDTracker: A Comprehensive Language Description for Visual Tracking UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:43:56.023454Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0733c883-5047-46e1-a4be-5ad5042674f3 · inbound
Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art cites this paper.
Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:18:36.983399Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3b9efa8-24fa-49ea-b772-795c4874a345 · inbound
Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection cites this paper.
Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T10:32:46.433867Z
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.