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Paper Citation Record · LEDGER

RDD2022: A multi-national image dataset for automatic Road Damage Detection

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2209.08538.

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

pith.paper-citation-record.v1
2209.08538 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:02:10.008281Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 a759a656-c1d1-4038-bc52-68f13823cf47 · inbound

YOLO-ROC: A High-Precision and Ultra-Lightweight Model for Real-Time Road Damage Detection cites this paper.

YOLO-ROC: A High-Precision and Ultra-Lightweight Model for Real-Time Road Damage Detection RDD2022: A multi-national image dataset for automatic Road Damage Detection

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T11:02:10.008281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:02:10.008281Z digest=sha256:f3806e426f99eb68e460f2113302e04d221c6447b72f57a399a6d3b0e2e6a925

Observation f8ff8477-58b2-49a8-8345-d1d3962e918b · inbound

Joint Training of Image Generator and Detector for Road Defect Detection cites this paper.

Joint Training of Image Generator and Detector for Road Defect Detection RDD2022: A multi-national image dataset for automatic Road Damage Detection

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T10:57:39.180014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:57:39.180014Z digest=sha256:12c64de0a87d22c6e1ad1ecf3dda87103d2b1d497508b89a9f673b32703f8d4f

Observation d0009a09-8805-44ca-9d3a-f320b4b9b3bc · inbound

PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification cites this paper.

PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification RDD2022: A multi-national image dataset for automatic Road Damage Detection

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T14:32:49.334913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:32:49.334913Z digest=sha256:4451497b47438e9db01ba91d0f189b64f48f05f2dd1f29ab59a04d192d26cb0e

Observation d85fdd23-90f4-4843-9a5c-e97235b45c9e · inbound

Automated Road Crack Localization for Spatially Guided Highway Maintenance cites this paper.

Automated Road Crack Localization for Spatially Guided Highway Maintenance RDD2022: A multi-national image dataset for automatic Road Damage Detection

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-16T12:32:52.511088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-16T12:31:49.629269Z digest=sha256:fa080e6a6e1f8d523c5cc6a8bb1a6ec0f02788cca015456d6eeedebe84d0f573

Observation 5df3c13b-3229-415a-8e1f-d0b017561eab · inbound

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment cites this paper.

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment RDD2022: A multi-national image dataset for automatic Road Damage Detection

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:41:24.390173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T17:30:39.410040Z digest=sha256:4d60587158e16f91cee456c535f76227d4e22d3491e79feb56487b555ecc8dbd