Paper Citation Record · LEDGER

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data

As of 15 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2507.13852.

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

pith.paper-citation-record.v1
2507.13852 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:18:45.626639Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

17 of 17 outbound references displayed

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verified fuzzy14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d417a226-cf9b-4e53-81eb-8cebc8090a77 · outbound

This paper cites Automatic building extraction from google earth images under complex backgrounds based on deep instance segmentation network,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Automatic building extraction from google earth images under complex backgrounds based on deep instance segmentation network,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:54.047577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b08f59c9-03ac-477f-83a6-60ad2e4c6748 · outbound

This paper cites Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:53.779241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a2f2fe65-f258-46a9-9524-22dac2fcfd6c · outbound

This paper cites Semantic segmentation of satellite images with different building types using deep learning methods,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Semantic segmentation of satellite images with different building types using deep learning methods,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:53.566934Z

Source-reported events for the cited work

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Observation ae11e1c1-846c-4425-b5bb-2921b76c1785 · outbound

This paper cites Large-scale individual building extraction from open-source satellite imagery via super- resolution-based instance segmentation approach,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Large-scale individual building extraction from open-source satellite imagery via super- resolution-based instance segmentation approach,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:53.353744Z

Source-reported events for the cited work

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Observation bd89fc50-a878-4940-87c9-17ddb7327f24 · outbound

This paper cites Benchmark for building segmentation on up-scaled sentinel-2 imagery,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Benchmark for building segmentation on up-scaled sentinel-2 imagery,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:53.008042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b67e406-7e74-415a-a4ea-f761dfa7fc1b · outbound

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

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 6

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Source-reported events for the cited work

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Observation 8a7284b4-f257-4a61-8a03-fb349486094c · outbound

This paper cites A u-net architecture for building segmentation through very high resolution cosmo-skymed imagery,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data A u-net architecture for building segmentation through very high resolution cosmo-skymed imagery,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:52.520202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 725b6080-bafc-498b-9bfd-4ab3e8554a10 · outbound

This paper cites Fine building segmentation in high-resolution SAR images via selective pyramid dilated network,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Fine building segmentation in high-resolution SAR images via selective pyramid dilated network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:48.182279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1ae31a1b-5e54-4dfa-9ef7-fd62f38964b9 · outbound

This paper cites Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC 2 MFN) for Building Segmentation of InSAR images,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC 2 MFN) for Building Segmentation of InSAR images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:47.871816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c2157b1-ec57-4a0c-82ff-ae2e6e0c91b0 · outbound

This paper cites Development of a dual-attention U-Net model for sea ice and open water classification on SAR images,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Development of a dual-attention U-Net model for sea ice and open water classification on SAR images,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:47.610447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b3292de9-cc98-4f85-85c8-79f3951b3e0d · outbound

This paper cites DeepMAO: Deep Multi-Scale Aware Overcomplete Network for Building Segmentation in Satellite Imagery,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data DeepMAO: Deep Multi-Scale Aware Overcomplete Network for Building Segmentation in Satellite Imagery,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:47.361248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 93799311-bbd7-4bb0-8488-83aae7dc91d3 · outbound

This paper cites Analyz- ing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Analyz- ing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:47.116448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 007de2cf-afbf-40d5-a5e2-419ed4088d5f · outbound

This paper cites Urban land cover classification from sentinel-2 images with quantum-classical network,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Urban land cover classification from sentinel-2 images with quantum-classical network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:46.823944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3c251fd8-d021-4b21-9892-fcd6ed8e5a32 · outbound

This paper cites Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:18:45.997204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c2b01dac-134f-48fa-a639-22ab0b9fa4aa · outbound

This paper cites Qspecklefilter: A quantum machine learning approach for sar speckle filtering,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Qspecklefilter: A quantum machine learning approach for sar speckle filtering,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:46.600807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c66424bd-5e8a-4922-8cde-6068412583ae · outbound

This paper cites Data encoding patterns for quantum computing,.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Data encoding patterns for quantum computing,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:18:46.314357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fee2f79c-9bff-44bf-986d-e31cce983f20 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data Attention U-Net: Learning Where to Look for the Pancreas

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:18:45.626639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.