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Paper Citation Record · LEDGER

Panoptic Segmentation of Environmental UAV Images : Litter Beach

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2508.15985.

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

pith.paper-citation-record.v1
2508.15985 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:37:12.312466Z

measured 30 of 30 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

  • verified exact7
  • verified fuzzy7
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8f8e281-9cb7-4593-9a1b-5b964ea69c6c · outbound

This paper cites write newline.

Panoptic Segmentation of Environmental UAV Images : Litter Beach write newline

Reference 1

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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.

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Observation 263a6b4c-9615-438f-9d50-036d3506f68c · outbound

This paper cites Déchets marins.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Déchets marins

Reference 2

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raw_fallback, observed 2026-08-05T17:37:15.282710Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 46126287-f854-456c-a06f-84ec1ef5eab8 · outbound

This paper cites Beach Litter Sampling Strategies: Is There a ‘Best’Method?.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Beach Litter Sampling Strategies: Is There a ‘Best’Method?

Reference 3

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raw_fallback, observed 2026-08-05T17:37:15.124385Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 7d55e324-98d0-4474-9b88-556848c6ca90 · outbound

This paper cites Guideline for Monitoring Marine Litter on the Beaches in the OSPAR Maritime Area. Edition 1.0.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Guideline for Monitoring Marine Litter on the Beaches in the OSPAR Maritime Area. Edition 1.0

Reference 4

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doi, observed 2026-08-05T17:37:13.585459Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 00008c78-0cdf-4c75-a7f6-31175fc101a5 · outbound

This paper cites Use of Unmanned Aerial Vehicles for Efficient Beach Litter Monitoring.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Use of Unmanned Aerial Vehicles for Efficient Beach Litter Monitoring

Reference 5

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raw_fallback, observed 2026-08-05T17:37:14.900980Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 241b5712-2858-4a8d-9275-5c95b25ee8ac · outbound

This paper cites Mapping Marine Litter Using UAS on a Beach-Dune System: A Multidisciplinary Approach.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Mapping Marine Litter Using UAS on a Beach-Dune System: A Multidisciplinary Approach

Reference 6

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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.

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Observation 582e0cae-1360-417d-84a5-7052c66aa03b · outbound

This paper cites Deep Learning and Remote Sensing: Detection of Dumping Waste Using UAV.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Deep Learning and Remote Sensing: Detection of Dumping Waste Using UAV

Reference 7

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doi, observed 2026-08-05T17:37:13.410653Z

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Observation 0e3af66d-c367-4823-938a-72c42fc2bc52 · outbound

This paper cites an unresolved cited work.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Unresolved cited work

Reference 9

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c2da7910-32cc-48b4-b940-d39f15bc8c59 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 10

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Observation 5b61c107-24d3-4236-824f-681b0f205f29 · outbound

This paper cites Machine Learning for Aquatic Plastic Litter Detection, Classification and Quantification (APLASTIC-Q).

Panoptic Segmentation of Environmental UAV Images : Litter Beach Machine Learning for Aquatic Plastic Litter Detection, Classification and Quantification (APLASTIC-Q)

Reference 11

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 6bb64831-0965-44e4-b183-0c6cdaac54f8 · outbound

This paper cites Real-Time UAV Trash Monitoring System.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Real-Time UAV Trash Monitoring System

Reference 12

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doi, observed 2026-08-05T17:37:13.029894Z

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.

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Observation 05cee507-f995-42cd-a2d5-8a3788c48956 · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Panoptic Segmentation of Environmental UAV Images : Litter Beach You Only Look Once: Unified, Real-Time Object Detection

Reference 13

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Source-reported events for the cited work

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Observation 33c6faf3-97da-4166-919a-9e7d455b1751 · outbound

This paper cites Xception: Deep Learning with Depthwise Separable Convolutions.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Xception: Deep Learning with Depthwise Separable Convolutions

Reference 14

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Observation 70026c51-0793-449a-ba47-e7150f912955 · outbound

This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Panoptic Segmentation of Environmental UAV Images : Litter Beach DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Reference 15

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raw_fallback, observed 2026-08-05T17:37:14.029321Z

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.

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Observation 7f994eed-cb3a-4905-961f-0002148b2df1 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Panoptic Segmentation of Environmental UAV Images : Litter Beach U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 16

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Observation fd97938b-c5cf-455a-96b7-2ca5f47f49b7 · outbound

This paper cites Fully Convolutional Networks for Semantic Segmentation.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Fully Convolutional Networks for Semantic Segmentation

Reference 17

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no resolver link, observed 2026-08-05T17:37:11.090799Z

Source-reported events for the cited work

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Observation d7e0b6f8-2685-4116-a384-a670c3d8f940 · outbound

This paper cites Deep High-Resolution Representation Learning for Visual Recognition.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Deep High-Resolution Representation Learning for Visual Recognition

Reference 18

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Observation 80a26aa9-bcf6-4ee7-a2e9-b239bc0a517d · outbound

This paper cites Adaptive Spatial Pooling for Image Classification.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Adaptive Spatial Pooling for Image Classification

Reference 19

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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.

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Observation c57509ef-4da6-41c9-bb8a-5f10dc62d4fc · outbound

This paper cites Pyramid Scene Parsing Network.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Pyramid Scene Parsing Network

Reference 20

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Observation 921dedbf-7c62-4626-9fe1-3c7acfbf6adc · outbound

This paper cites DeeperLab: Single-Shot Image Parser.

Panoptic Segmentation of Environmental UAV Images : Litter Beach DeeperLab: Single-Shot Image Parser

Reference 21

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Observation a11ba7c5-bb80-49eb-9918-193b67bc56e4 · outbound

This paper cites Mask R-CNN.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Mask R-CNN

Reference 22

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Source-reported events for the cited work

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Observation 6f2b7885-ffcd-4c25-a6c9-cd00fb54998c · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Panoptic Segmentation of Environmental UAV Images : Litter Beach EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 23

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Observation 2f77e6c9-c93b-4962-a808-25d48b555f0c · outbound

This paper cites PolarMask: Single Shot Instance Segmentation with Polar Representation.

Panoptic Segmentation of Environmental UAV Images : Litter Beach PolarMask: Single Shot Instance Segmentation with Polar Representation

Reference 24

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verified exact
local_arxiv, observed 2026-08-05T17:37:12.612367Z

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.

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Observation 21dc5c17-c78d-47b9-a496-657c0131eff9 · outbound

This paper cites Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

Reference 25

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no resolver link, observed 2026-08-05T17:37:11.767418Z

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source=arxiv_source observed=2026-08-05T17:37:11.767418Z digest=sha256:db0770fdf04bf2e360c2f46f1162b527539077b1448cb7591110d9d8282c0b79

Observation 7bc5d581-1b9d-4d47-8541-6142cb363839 · outbound

This paper cites EfficientPS: Efficient Panoptic Segmentation.

Panoptic Segmentation of Environmental UAV Images : Litter Beach EfficientPS: Efficient Panoptic Segmentation

Reference 26

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local_arxiv, observed 2026-08-05T17:37:12.512259Z

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=arxiv_source observed=2026-08-05T17:37:11.883129Z digest=sha256:7bb705d6e52df49a4369cf54ccd55bed2b26a93ef15fd16107bd799b90be09f1

Observation 1a4d4027-8657-4757-bf4a-cb9c6a1b7329 · outbound

This paper cites Fast Panoptic Segmentation Network.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Fast Panoptic Segmentation Network

Reference 27

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local_arxiv, observed 2026-08-05T17:37:13.225410Z

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.

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Observation 1d947948-d71c-4bc9-be0f-c7de8b5b5904 · outbound

This paper cites Density and Composition of Surface and Buried Plastic Debris in Beaches of Senegal.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Density and Composition of Surface and Buried Plastic Debris in Beaches of Senegal

Reference 28

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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.

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Observation 0523f944-9b9e-4bce-a824-7bd9161b3d6f · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Panoptic Segmentation of Environmental UAV Images : Litter Beach Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 29

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Observation fff18b6d-0597-42fb-b1e2-a282e0f5ba10 · outbound

This paper cites , " * write output.state after.block = add.period write.

Panoptic Segmentation of Environmental UAV Images : Litter Beach , " * write output.state after.block = add.period write

Reference 30

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Unavailable: canonical work link unavailable.

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Observation 866f12cc-d342-4562-85ee-9a5bb2330855 · outbound

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Panoptic Segmentation of Environmental UAV Images : Litter Beach write newline

Reference 31

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Pith citing papers

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