Paper Citation Record · LEDGER

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.16269.

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

pith.paper-citation-record.v1
2306.16269 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:12:07.536466Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:25:46.694841Z

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 bb1f33c0-32cb-4ea6-b9c1-1dd529b0cc5f · inbound

fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model cites this paper.

fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T17:12:07.536466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dcbade4-619b-4277-b743-b2ad06515aec · inbound

SOPSeg: Prompt-based Small Object Instance Segmentation in Remote Sensing Imagery cites this paper.

SOPSeg: Prompt-based Small Object Instance Segmentation in Remote Sensing Imagery RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T11:16:30.027074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2671bec4-cc09-4207-b723-6a202d83ad42 · inbound

HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning cites this paper.

HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:25:46.696496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 540887d5-0d06-43f9-9e6c-6cbdddfb8887 · inbound

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation cites this paper.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.