Paper Citation Record · LEDGER

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery

As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.06406.

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

pith.paper-citation-record.v1
2608.06406 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

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measured 21 of 21 standing notices

One-hop event checks from named stored sources.

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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

21 of 21 outbound references displayed

verified exact2
verified fuzzy5
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b98ac3d-6a7f-4d22-ad6d-7894e9404636 · outbound

This paper cites an unresolved cited work.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:30:42.557340Z

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Observation 32b0b559-8e07-4fd6-8325-18a2cc97d594 · outbound

This paper cites In: Advances in Neural Information Processing Systems (2020).

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In: Advances in Neural Information Processing Systems (2020)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:30:42.547828Z

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Observation e34490d7-e1c5-441e-bf97-08d61717dba4 · outbound

This paper cites Inter- national Journal of Applied Earth Observation and Geoinformation143, 104814 (2025).https://doi.org/https://doi.org/10.1016/j.jag.2025.104814.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Inter- national Journal of Applied Earth Observation and Geoinformation143, 104814 (2025).https://doi.org/https://doi.org/10.1016/j.jag.2025.104814

Reference 3

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Observation f7ccc0e2-9587-4a6a-8b31-f95e23d58e20 · outbound

This paper cites In: Proceedings of the 33rd International Conference on Machine Learning (2016).

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In: Proceedings of the 33rd International Conference on Machine Learning (2016)

Reference 4

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Observation 1f3cc143-98d0-4e0b-89b5-dfad90c29a10 · outbound

This paper cites Artificial Intelligence Review 56(S1), 1513–1589 (Oct 2023).https://doi.org/10.1007/s10462-023-10562-9.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Artificial Intelligence Review 56(S1), 1513–1589 (Oct 2023).https://doi.org/10.1007/s10462-023-10562-9

Reference 5

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Source-reported events for the cited work

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Observation 5021806c-d60f-410a-854f-66b7d698c3f9 · outbound

This paper cites Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' View.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' View

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:42.014995Z

Source-reported events for the cited work

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Observation d99532c8-19c9-47d1-8e3d-fb4410104f3e · outbound

This paper cites In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 7

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Observation 0a5f70eb-d94d-4937-aa7e-f0bedc5c5f54 · outbound

This paper cites Machine Learning110(3), 457–506 (Mar 2021).https://doi.org/10.1007/s10994-021-05946-3 10 L.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Machine Learning110(3), 457–506 (Mar 2021).https://doi.org/10.1007/s10994-021-05946-3 10 L

Reference 8

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Source-reported events for the cited work

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Observation c6be9f8c-a1a4-481e-9eb4-ad51bfb5a2e5 · outbound

This paper cites Remote Sensing15(8), 1969 (Jan 2023).https://doi.org/10.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Remote Sensing15(8), 1969 (Jan 2023).https://doi.org/10

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:30:42.528860Z

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Observation 7723490e-36f9-443f-8abd-066c6653d892 · outbound

This paper cites an unresolved cited work.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:30:42.518955Z

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Observation 73876b77-c952-4dd9-8f62-9a3293bc544a · outbound

This paper cites Ambio52(11), 1804–1818 (Nov 2023).https://doi.org/10.1007/s13280-023-01908-2.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Ambio52(11), 1804–1818 (Nov 2023).https://doi.org/10.1007/s13280-023-01908-2

Reference 11

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Observation 07f5d618-4291-48ed-9149-e8ca89c672b5 · outbound

This paper cites In: Proceedings of the 35th International Conference on Machine Learning.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In: Proceedings of the 35th International Conference on Machine Learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:30:42.507886Z

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Observation 3ecae286-5c7b-4abe-a226-4b8ab50b16d2 · outbound

This paper cites an unresolved cited work.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 03752c32-ea04-4f1a-a121-d058584bf2ae · outbound

This paper cites Nature Ecology & Evolution7(11), 1778–1789 (Nov 2023).

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Nature Ecology & Evolution7(11), 1778–1789 (Nov 2023)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:42.042610Z

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Observation 84cb674e-9ceb-47bb-9dfc-95ba9aa9357b · outbound

This paper cites Remote Sensing of Environment268, 112760 (Jan 2022).https://doi.org/10.1016/j.rse.2021.112760.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Remote Sensing of Environment268, 112760 (Jan 2022).https://doi.org/10.1016/j.rse.2021.112760

Reference 15

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Observation 9560d937-7336-46a8-a14f-b55b7859479f · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelli- gence37(8), 9134–9142 (2023).https://doi.org/10.1609/aaai.v37i8.26096.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Proceedings of the AAAI Conference on Artificial Intelli- gence37(8), 9134–9142 (2023).https://doi.org/10.1609/aaai.v37i8.26096

Reference 16

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This paper cites Remote Sensing of Environment253, 112165 (Feb 2021).https://doi.org/10.1016/j.rse.2020.112165.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Remote Sensing of Environment253, 112165 (Feb 2021).https://doi.org/10.1016/j.rse.2020.112165

Reference 17

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Observation 68fc3ee6-8da3-40ef-a872-9eac8d757b80 · outbound

This paper cites In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:42.055007Z

Source-reported events for the cited work

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Observation 345a2b91-2a19-4226-add0-be18d1b28836 · outbound

This paper cites In- ternational Journal of Applied Earth Observation and Geoinformation128, 103711 (Apr 2024).https://doi.org/10.1016/j.jag.2024.103711.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In- ternational Journal of Applied Earth Observation and Geoinformation128, 103711 (Apr 2024).https://doi.org/10.1016/j.jag.2024.103711

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:42.058150Z

Source-reported events for the cited work

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Observation 2f4cb56c-68c7-400f-80f0-08710c461655 · outbound

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

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery In: Advances in Neural Information Processing Systems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:30:42.489174Z

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Observation 2b152e67-f484-4a87-8503-4d11b7cff535 · outbound

This paper cites Scientific Data12(1), 678 (Apr 2025).https: //doi.org/10.1038/s41597-025-05021-9.

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery Scientific Data12(1), 678 (Apr 2025).https: //doi.org/10.1038/s41597-025-05021-9

Reference 21

Resolution: verified exact; doi, observed 2026-08-10T04:30:42.094892Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.