Paper Citation Record · LEDGER

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.06829.

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

pith.paper-citation-record.v1
2607.06829 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-10T20:29:13.802951Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a2378829-2fec-40f5-acd6-239d1627d387 · outbound

This paper cites Vectorization and parity errors,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Vectorization and parity errors,

Reference 1

Resolution: verified exact; doi, observed 2026-07-10T20:37:34.001753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f8063e2b-9c6d-4add-ab3a-134016b1fd06 · outbound

This paper cites Smoothing and compression of lines obtained by raster-to-vector conversion,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Smoothing and compression of lines obtained by raster-to-vector conversion,

Reference 2

Resolution: verified exact; doi, observed 2026-07-10T20:37:34.024891Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 393d1fa5-a0d6-4e84-8b2e-5b566c6963f7 · outbound

This paper cites Smoothing a network of planar polygonal lines obtained with vectorization,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Smoothing a network of planar polygonal lines obtained with vectorization,

Reference 3

Resolution: verified exact; doi, observed 2026-07-10T20:37:34.009832Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 965ced06-d83a-4258-826f-07d5b53158ea · outbound

This paper cites Dynamic time warping algorithm review,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Dynamic time warping algorithm review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.772950Z

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Observation 6d86e7a1-b37c-4a9d-919c-f4f02445edb3 · outbound

This paper cites Automated extraction of 3-D railway tracks from mobile laser scanning point clouds,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Automated extraction of 3-D railway tracks from mobile laser scanning point clouds,

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:37:33.999386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 39298fc0-2cbe-4ddb-8332-254cd73786e5 · outbound

This paper cites Positive train control (PTC),.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Positive train control (PTC),

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.767338Z

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Observation c4f0670d-c53b-4399-a668-21c782e7545d · outbound

This paper cites Available: https://railroads.dot.gov/research-development/program-areas/train-control/ptc/ positive-train-control-ptc.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Available: https://railroads.dot.gov/research-development/program-areas/train-control/ptc/ positive-train-control-ptc

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.769217Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a4120b3e-28b4-4e1b-beda-0296fb9d46c8 · outbound

This paper cites Three emerging LiDAR applications for rail data capture,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Three emerging LiDAR applications for rail data capture,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.771055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 05e5940c-072f-4193-a5a5-45fdcb85467f · outbound

This paper cites A fast algorithm for rail extraction using mobile laser scanning data,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network A fast algorithm for rail extraction using mobile laser scanning data,

Reference 9

Resolution: verified exact; doi, observed 2026-07-10T20:37:34.022340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e2066fc5-df4c-4427-a86f-e5f178609f6d · outbound

This paper cites Semantic segmentation of point clouds with PointNet and KPConv architectures applied to railway tunnels,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Semantic segmentation of point clouds with PointNet and KPConv architectures applied to railway tunnels,

Reference 10

Resolution: verified exact; doi, observed 2026-07-10T20:37:33.995866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99173bce-e055-4acc-a59b-26a765d0ea66 · outbound

This paper cites Point cloud semantic segmentation of complex railway environments using deep learning,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Point cloud semantic segmentation of complex railway environments using deep learning,

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:37:34.017262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d68b34cb-7bfe-402e-a3c4-4ade0bb1dc5d · outbound

This paper cites Multimodal deep learning for point cloud panoptic segmentation of railway environments,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Multimodal deep learning for point cloud panoptic segmentation of railway environments,

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:37:33.990827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be8c167d-dbe9-429d-846f-7fce2e77c551 · outbound

This paper cites Automatic extraction of railroad centerlines from mobile laser scanning data,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Automatic extraction of railroad centerlines from mobile laser scanning data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.801262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 73267d4e-70c2-4768-909a-431184eaf462 · outbound

This paper cites Rail track detection and projection-based 3D modeling from UAV point cloud,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Rail track detection and projection-based 3D modeling from UAV point cloud,

Reference 14

Resolution: verified exact; doi, observed 2026-07-10T20:37:34.004028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3ad28c0f-786b-4b87-8068-cf374c0b5816 · outbound

This paper cites Fully automated methodology for the delineation of railway lanes and the generation of IFC alignment models using 3D point cloud data,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Fully automated methodology for the delineation of railway lanes and the generation of IFC alignment models using 3D point cloud data,

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:37:34.007459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bc6415ff-1292-4f01-9d45-88deda1913e5 · outbound

This paper cites Fully automated extraction of railtop centerline from mobile laser scanning data,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Fully automated extraction of railtop centerline from mobile laser scanning data,

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:37:34.028189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ed824348-2482-4df2-a4da-3b2efeef4b5f · outbound

This paper cites ACNN: a Full Resolution DCNN for Medical Image Segmentation.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network ACNN: a Full Resolution DCNN for Medical Image Segmentation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:37:34.458154Z

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Observation 64142247-3cc1-4336-9f96-7c254b0430c1 · outbound

This paper cites Classify rail points in mobile lidar point clouds,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Classify rail points in mobile lidar point clouds,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.804321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ac1f30b-2a5a-40c0-8c25-e843a6b8269d · outbound

This paper cites Prepare point cloud training data (3D Analyst),.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Prepare point cloud training data (3D Analyst),

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.807208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2103af43-7814-45a1-b490-8c83ded0ee19 · outbound

This paper cites Train point cloud classification model (3D Analyst),.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Train point cloud classification model (3D Analyst),

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.794830Z

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Observation be08c865-1579-4944-b8ef-0353dea289c4 · outbound

This paper cites Classify point cloud using trained model (3D Analyst),.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Classify point cloud using trained model (3D Analyst),

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.792585Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41a1f04b-c879-493f-a38c-c9e4f252ff99 · outbound

This paper cites Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.809106Z

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Observation 61793cb2-6a7d-43d2-8cc5-124499efc5dd · outbound

This paper cites Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8fc773bc-b397-4dd8-bbe0-dee8597bc86d · outbound

This paper cites U-Net for brain segmentation,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network U-Net for brain segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.786251Z

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Observation 9a72b6e2-3a79-43b2-b05d-2399b55f308d · outbound

This paper cites 9351, pp.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network 9351, pp

Reference 25

Resolution: metadata mismatch; doi, observed 2026-07-10T20:37:34.019935Z

Source-reported events for the cited work

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Observation 8f050a0c-e8b7-440e-a090-1f0415782a46 · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Multi-Scale Context Aggregation by Dilated Convolutions

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:37:34.455301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c351f4d-5317-4f42-a958-d9a58bf4427b · outbound

This paper cites ACNN: a full resolution DCNN for medical image segmentation,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network ACNN: a full resolution DCNN for medical image segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.782450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6254df2e-1531-4ea4-8dc3-3b768b7b2267 · outbound

This paper cites Available: https://github.com/XiaoYunZhou27/ACNN.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Available: https://github.com/XiaoYunZhou27/ACNN

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.784402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e46a3ef6-8497-40c5-8d89-7eb1b4372f8e · outbound

This paper cites Batch normalization: accelerating deep network training by reducing internal covariate shift,.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Batch normalization: accelerating deep network training by reducing internal covariate shift,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.776180Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a12bdcb-531a-43c1-be0f-d0c29826bb6b · outbound

This paper cites [Online].

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network [Online]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.778766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c45ff3a2-bea7-4349-9304-ddaf173faa26 · outbound

This paper cites What is ArcScan?.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network What is ArcScan?

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.799285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d1655db-be68-4885-bb0f-e00a0f96686d · outbound

This paper cites Efficient Computation of the Directional Extremal Boundary of a Union of Equal-Radius Circles.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Efficient Computation of the Directional Extremal Boundary of a Union of Equal-Radius Circles

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:37:34.452538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22d9b46a-adda-4330-8e19-c4ab8b65ef44 · outbound

This paper cites Extract rails from point cloud (3D Analyst),.

Rail Track Extraction from Rasterized Classified Point Clouds Using a Full-Resolution, Fully Convolutional Recurrent Neural Network Extract rails from point cloud (3D Analyst),

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:37:34.790628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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