Paper Citation Record · LEDGER

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.10841.

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

pith.paper-citation-record.v1
2607.10841 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T08:52:51.503388Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

34 of 34 outbound references displayed

verified exact0
verified fuzzy0
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e3db9df-3629-46d5-969e-a01216cb1711 · outbound

This paper cites Nature645(8080), 399–406 (2025).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Nature645(8080), 399–406 (2025)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea18bc2f-fc0a-4d4f-b509-1537f9cef7f9 · outbound

This paper cites figshare (2016).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels figshare (2016)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d70ecd38-81c4-4fa8-9439-26835ec80cfc · outbound

This paper cites In: British Machine Vision Conference (2019).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: British Machine Vision Conference (2019)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f6f83b8-c868-418d-98cd-3f9435af7653 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

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Unavailable: canonical work link unavailable.

Observation d37127fe-c7d2-426a-a74d-af0b1a0383b8 · outbound

This paper cites In: International Conference on Learning Representations (ICLR) (2021).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: International Conference on Learning Representations (ICLR) (2021)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca726796-0d87-44d6-8257-f421f980ac74 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39ea507f-a9a0-4273-be6d-905a32f54553 · outbound

This paper cites In: ACM Conference on Computing and Sustainable Societies (SIGCAS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: ACM Conference on Computing and Sustainable Societies (SIGCAS)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9639308-0b39-40f9-b8df-6fead9fb02eb · outbound

This paper cites In: Asian Conference on Computer Vision (ACCV).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Asian Conference on Computer Vision (ACCV)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08a416c7-ca36-4c2d-b6ce-61df26aef2bb · outbound

This paper cites In: International Geoscience and Remote Sensing Symposium (IGARSS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: International Geoscience and Remote Sensing Symposium (IGARSS)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1f379f5-bce3-43d2-b497-97ef012980a2 · outbound

This paper cites In: Computer Vision and Pattern Recognition (CVPR).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Computer Vision and Pattern Recognition (CVPR)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cf91fb4-30b7-4f54-8b0c-b11c8352db66 · outbound

This paper cites Science of Remote Sensing p.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Science of Remote Sensing p

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f794465-1c1b-4c11-9190-d15e72eea960 · outbound

This paper cites In: Computer Vision and Pattern Recognition (CVPR).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Computer Vision and Pattern Recognition (CVPR)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85ec70d9-ea96-4b47-a0fd-59a0f7efdb40 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a60aad6-13a6-4366-a23c-944ca22d24ce · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Advances in Neural Information Processing Systems (NeurIPS)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c139acc-752e-4f13-8ccd-731c5a565104 · outbound

This paper cites In: Computer Vision and Pattern Recognition (CVPR) (2019).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Computer Vision and Pattern Recognition (CVPR) (2019)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05ee69b7-61b5-4193-8f7c-3cd5edc8c859 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a1fbc6a-16a4-4038-a5f8-9a5645fe1258 · outbound

This paper cites In: Computer Vision and Pat- tern Recognition (CVPR).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Computer Vision and Pat- tern Recognition (CVPR)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e94f2110-276e-4533-8215-140afc0f892e · outbound

This paper cites In: Advances in Neural Informa- tion Processing Systems (NeurIPS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Advances in Neural Informa- tion Processing Systems (NeurIPS)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0873e17-ded0-45e9-9edf-a6abb8a4cb08 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64a2c549-a8c4-4ecb-9787-ff4a0efdbb6a · outbound

This paper cites In: International Conference on Learning Representations (ICLR) (2019).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: International Conference on Learning Representations (ICLR) (2019)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03cb0e7f-d5c9-4775-8513-fa52395b9aa6 · outbound

This paper cites In: Inter- national Geoscience and Remote Sensing Symposium (IGARSS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Inter- national Geoscience and Remote Sensing Symposium (IGARSS)

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2538d961-1aa2-4960-9769-2a5cb394d89b · outbound

This paper cites ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2, 275–282 (2022).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2, 275–282 (2022)

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdad6378-b207-4a98-ad92-bad7085905f3 · outbound

This paper cites org.https://www.openstreetmap.org(2017).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels org.https://www.openstreetmap.org(2017)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22772425-b64d-4b25-be6b-1b6586354b5c · outbound

This paper cites In: Medical Image Computing and Computer Assisted Intervention (MICCAI).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Medical Image Computing and Computer Assisted Intervention (MICCAI)

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dc7fb8e-2e6a-4849-b7dd-23a1089be965 · outbound

This paper cites International Journal of Computer Vision (IJCV)115(3), 211–252 (2015).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels International Journal of Computer Vision (IJCV)115(3), 211–252 (2015)

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f7b33bd-7bd0-4f72-93d5-3b102fa71e78 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75d6f36c-7d19-45a8-ac07-47f2725f9509 · outbound

This paper cites an unresolved cited work.

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3b9483a-05fc-493f-92cc-fece6ca7fee7 · outbound

This paper cites In: Advances in Neural Processing Systems (NeurIPS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Advances in Neural Processing Systems (NeurIPS)

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6636025-8dc8-4d57-8127-c57d645a2bdb · outbound

This paper cites In: International Conference on Learning Representations (ICLR) (2023).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: International Conference on Learning Representations (ICLR) (2023)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b77af97-9f28-470a-9a05-3cd2d569cafb · outbound

This paper cites In: European Conference on Computer Vision (ECCV) (2018).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: European Conference on Computer Vision (ECCV) (2018)

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c0a3d50-41a9-403d-930e-25613c11d7fa · outbound

This paper cites In: Medical Image Computing and Computer Assisted Intervention (MICCAI).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Medical Image Computing and Computer Assisted Intervention (MICCAI)

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b8e9a4d-0157-4946-bcab-b48e8ae4cbbe · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Advances in Neural Information Processing Systems (NeurIPS)

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37732b15-1178-4070-af6d-ad2c2e5aca7b · outbound

This paper cites In: Medical Image Computing and Computer Assisted Intervention (MICCAI).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: Medical Image Computing and Computer Assisted Intervention (MICCAI)

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ee46e20-c326-45a9-9bbe-7bbf8e7253e0 · outbound

This paper cites In: International Geoscience and Remote Sensing Symposium (IGARSS).

Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels In: International Geoscience and Remote Sensing Symposium (IGARSS)

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T08:52:51.503388Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.