Paper Citation Record · LEDGER

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2502.05476.

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

pith.paper-citation-record.v1
2502.05476 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:14:18.883625Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

24 of 24 outbound references displayed

verified exact13
verified fuzzy9
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94883502-a11e-4892-897f-77c9dd633376 · outbound

This paper cites Electron Markets 31(6), 685–695 (2021).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Electron Markets 31(6), 685–695 (2021)

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31ca1cd9-71a4-4ad2-917d-cde04395b298 · outbound

This paper cites Pattern Recognition Letters 141, 61–67 (2021).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Pattern Recognition Letters 141, 61–67 (2021)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T19:14:18.798849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab89fa31-0297-4ef0-860f-eb196bfe1926 · outbound

This paper cites Procedia Technology 24, 1366 –1373 (2016).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Procedia Technology 24, 1366 –1373 (2016)

Reference 3

Resolution: verified exact; doi, observed 2026-08-08T19:14:18.964678Z

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Observation d62411ec-49de-4719-9788-7a195e3274f3 · outbound

This paper cites 2018 Baltic URSI Symposium (URSI), Poznan, Poland, pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture 2018 Baltic URSI Symposium (URSI), Poznan, Poland, pp

Reference 4

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Observation 004bfe26-6e6f-4ed4-b9ca-bce620d61e86 · outbound

This paper cites 2016 IEEE 19th Interna- tional Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture 2016 IEEE 19th Interna- tional Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, pp

Reference 5

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T19:14:20.129487Z

Source-reported events for the cited work

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Observation d267eb25-5596-4ab8-97bc-839e0a1edb3d · outbound

This paper cites IEEE Transactions on Sys- tems, Man, and Cybernetics 8(4), 237 –247 (1978).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture IEEE Transactions on Sys- tems, Man, and Cybernetics 8(4), 237 –247 (1978)

Reference 6

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T19:14:19.962472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a2152c30-3334-47dd-b371-41928403f226 · outbound

This paper cites 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Tirun elveli, India, pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Tirun elveli, India, pp

Reference 7

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T19:14:19.778565Z

Source-reported events for the cited work

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Observation 6c4cc62b-e26e-4d24-b582-7e25f00a6150 · outbound

This paper cites In: 2021 28th Conference of Open Innovations As- sociation (FRUCT), pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: 2021 28th Conference of Open Innovations As- sociation (FRUCT), pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.425477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 82061ef8-c2ab-4219-98f8-fd35bdcb8acc · outbound

This paper cites Scientific Reports, vol.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Scientific Reports, vol

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.414807Z

Source-reported events for the cited work

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Observation 91e59aa5-7aa7-4cba-8438-16c7ee47d81a · outbound

This paper cites In: 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineer- ing (EIConRus), pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineer- ing (EIConRus), pp

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.403398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b930ae6b-4a24-41c4-9c14-deb11026c0e0 · outbound

This paper cites In: 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.391685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 83a63e90-c2b1-4fbc-a4c3-9138b4f9f273 · outbound

This paper cites In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, pp

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0cde9639-4432-4b42-87e9-c6e1d36510c6 · outbound

This paper cites IEEE Transactions on Medical Imaging, vol.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture IEEE Transactions on Medical Imaging, vol

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.368190Z

Source-reported events for the cited work

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Observation 3d6593c0-654b-463a-bda0-a04c3ac52979 · outbound

This paper cites In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.357657Z

Source-reported events for the cited work

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Observation c3a0229e-9efa-4a3b-aad8-cb104f34d8f9 · outbound

This paper cites In: 2019 Systems of Signal Synchronization, Generating and Processing in Telecommuni- cations (SYNCHROINFO), pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: 2019 Systems of Signal Synchronization, Generating and Processing in Telecommuni- cations (SYNCHROINFO), pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:14:20.346631Z

Source-reported events for the cited work

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Observation 48f07671-27de-47b6-b830-5b52075bf943 · outbound

This paper cites Applying Knowledge Transfer for Water Body Segmentation in Peru.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Applying Knowledge Transfer for Water Body Segmentation in Peru

Reference 16

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Source-reported events for the cited work

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Observation 5f6ad922-7387-4748-a54b-4c59e85282ac · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing, vol.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture IEEE Transactions on Geoscience and Remote Sensing, vol

Reference 17

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Source-reported events for the cited work

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Observation 3c307394-a776-4917-81cf-0de8ce7f7ef4 · outbound

This paper cites Neural Networks 121, 74 –87 (2020).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Neural Networks 121, 74 –87 (2020)

Reference 18

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Observation af491c7c-c75e-4c27-ab91-adc0a6337182 · outbound

This paper cites Journal of Healthcare Engineering, vol.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Journal of Healthcare Engineering, vol

Reference 19

Resolution: verified exact; doi, observed 2026-08-08T19:14:18.941652Z

Source-reported events for the cited work

retraction dated 2023-10-18. Source: crossref record 10.1155/2023/9890389->10.1155/2022/4189781:retraction, observed 2026-07-11T02:53:59.260384+00:00. This notice travels one citation hop only.

retraction . Source: retraction watch record 61600, observed 2026-07-08T21:54:04.74669+00:00. This notice travels one citation hop only.

Observation 51295c74-3bf9-4f82-9e1b-bbde8986c314 · outbound

This paper cites IEEE Access 7, 44247 –44257 (2019).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture IEEE Access 7, 44247 –44257 (2019)

Reference 20

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Observation 05046033-7f36-45d2-8f98-ea0d345a1676 · outbound

This paper cites Pattern Recognition 44(4), 777 –787 (2011).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Pattern Recognition 44(4), 777 –787 (2011)

Reference 21

Resolution: verified exact; doi, observed 2026-08-08T19:14:18.929180Z

Source-reported events for the cited work

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Observation 8527481c-7bc8-4437-abb9-1165897445aa · outbound

This paper cites In: Dash, S., Acharya, B., Mittal, M., Abraham, A., Kelemen, A.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture In: Dash, S., Acharya, B., Mittal, M., Abraham, A., Kelemen, A

Reference 22

Resolution: verified exact; doi, observed 2026-08-08T19:14:18.916862Z

Source-reported events for the cited work

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Observation 77bc194a-96dd-47c9-b5fc-f3459609712f · outbound

This paper cites 2019 41st Annual International Conference of the IEEE Engineering in Medi- cine and Biology Society (EMBC), Berlin, Germany, pp.

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture 2019 41st Annual International Conference of the IEEE Engineering in Medi- cine and Biology Society (EMBC), Berlin, Germany, pp

Reference 23

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Observation 00dc923d-7bfb-451c-9723-09ef66e418a4 · outbound

This paper cites Applied Soft Computing 126, 109297 (2022).

Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture Applied Soft Computing 126, 109297 (2022)

Reference 24

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T19:14:19.208080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.