Paper Citation Record · LEDGER

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.24532.

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

pith.paper-citation-record.v1
2607.24532 v3

Coverage vector

measured 45 of 45 reference resolution

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measured 45 of 45 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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verified fuzzy9
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External citation measurements

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Outbound references

Observation c7f977e4-1ddb-4741-84d0-a5a12afc9693 · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing202, 682–690 (Aug 2023).https://doi.org/10.1016/j.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps ISPRS Journal of Photogrammetry and Remote Sensing202, 682–690 (Aug 2023).https://doi.org/10.1016/j

Reference 1

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Observation b1185da1-23ca-47ed-9fe7-fa4a04939755 · outbound

This paper cites Scientific Data9(1) (Jun 2022).https://doi.org/10.1038/s41597-022-01307-4, http://dx.doi.org/10.1038/s41597-022-01307-4.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Scientific Data9(1) (Jun 2022).https://doi.org/10.1038/s41597-022-01307-4, http://dx.doi.org/10.1038/s41597-022-01307-4

Reference 2

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Observation 2e219659-9f6a-4f50-89e7-b8456777d859 · outbound

This paper cites AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

Reference 3

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Observation b966c14c-4473-4d08-bef4-a78aaa2711b6 · outbound

This paper cites In: Duncanson, L., Disney, M., Armston, J., Minor, D., Camacho, F., Nickeson, J.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps In: Duncanson, L., Disney, M., Armston, J., Minor, D., Camacho, F., Nickeson, J

Reference 4

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Observation 75531ae2-a537-4f18-a344-6dac832a026b · outbound

This paper cites (eds.): 2006 IPCC Guidelines for National Greenhouse Gas Inventories.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps (eds.): 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Reference 5

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Observation df94fa57-992a-4255-a5c3-93026ee10f9d · outbound

This paper cites TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis

Reference 6

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Unresolved cited work

Reference 7

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Observation 19f58c67-2f8f-4562-a66f-4c1f5c03947d · outbound

This paper cites In: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sens- ing Symposium.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps In: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sens- ing Symposium

Reference 8

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Observation 1051aa66-a875-4c11-8015-7f0b1f99b236 · outbound

This paper cites arXiv preprint arXiv:2511.12104 (2025),https://arxiv.org/ abs/2511.12104.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps arXiv preprint arXiv:2511.12104 (2025),https://arxiv.org/ abs/2511.12104

Reference 9

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Observation 2b546a1a-f55e-444f-9898-e27f0b0ec5e2 · outbound

This paper cites Climate Policy20(9), 1112–1126 (2020).https://doi.org/ 10.1080/14693062.2020.1781035.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Climate Policy20(9), 1112–1126 (2020).https://doi.org/ 10.1080/14693062.2020.1781035

Reference 10

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Observation ff35ca7b-32a6-42ee-a59f-142b3ffc1454 · outbound

This paper cites Nature Communications14(1) (Jul 2023).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Nature Communications14(1) (Jul 2023)

Reference 11

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Observation 57ec68e6-ea3c-4f1b-9e1c-35776d87b363 · outbound

This paper cites arXiv preprint arXiv:2511.13655 (2025),https://arxiv.org/abs/2511.13655 Best Practices for EO Maps 11.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps arXiv preprint arXiv:2511.13655 (2025),https://arxiv.org/abs/2511.13655 Best Practices for EO Maps 11

Reference 12

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Observation c4b2a23e-0ec7-400e-aa9a-248878b31e05 · outbound

This paper cites Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations

Reference 13

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Observation 64e85cb1-5fd9-406c-a889-d0d9ee97518c · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 14

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Observation 1afc263c-6fc5-4ea8-9bbf-01fe62e22890 · outbound

This paper cites Remote Sensing of En- vironment330, 114951 (2025).https://doi.org/10.1016/j.rse.2025.114951, https://doi.org/10.1016/j.rse.2025.114951.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Remote Sensing of En- vironment330, 114951 (2025).https://doi.org/10.1016/j.rse.2025.114951, https://doi.org/10.1016/j.rse.2025.114951

Reference 15

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Observation 2185e26e-12f6-4084-813c-0127b8bf6e8e · outbound

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

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps In: Advances in Neural Information Processing Systems

Reference 16

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Observation 1728ffcd-3188-4321-80eb-ef37b056fe20 · outbound

This paper cites Nature Ecology & Evolution7, 1778–1789 (2023).https: //doi.org/10.1038/s41559-023-02206-6,https://doi.org/10.1038/s41559- 023-02206-6.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Nature Ecology & Evolution7, 1778–1789 (2023).https: //doi.org/10.1038/s41559-023-02206-6,https://doi.org/10.1038/s41559- 023-02206-6

Reference 17

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This paper cites Science Advances 9(37) (Sep 2023).https://doi.org/10.1126/sciadv.adh4097,http://dx.doi.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Science Advances 9(37) (Sep 2023).https://doi.org/10.1126/sciadv.adh4097,http://dx.doi

Reference 18

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This paper cites Na- ture Communications17(1) (Jan 2026).https://doi.org/10.1038/s41467-026- 68996-y,http://dx.doi.org/10.1038/s41467-026-68996-y.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Na- ture Communications17(1) (Jan 2026).https://doi.org/10.1038/s41467-026- 68996-y,http://dx.doi.org/10.1038/s41467-026-68996-y

Reference 19

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Observation f3fb5dca-91b6-436f-80b4-23ed2de1af41 · outbound

This paper cites Nature Communications16(1) (Jul 2025).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Nature Communications16(1) (Jul 2025)

Reference 20

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This paper cites The International Archives of the Photogrammetry, Remote 12 G.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps The International Archives of the Photogrammetry, Remote 12 G

Reference 21

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Unresolved cited work

Reference 22

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Observation 76bf2001-da36-4845-81f7-1a088fcbc35a · outbound

This paper cites Sci- entific Data12(1) (Nov 2025).https://doi.org/10.1038/s41597-025-06097-z, http://dx.doi.org/10.1038/s41597-025-06097-z.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Sci- entific Data12(1) (Nov 2025).https://doi.org/10.1038/s41597-025-06097-z, http://dx.doi.org/10.1038/s41597-025-06097-z

Reference 23

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Observation 17e19c5e-edcf-4d04-b614-2910fa1877c3 · outbound

This paper cites Remote Sensing of Environment148, 42–57 (May 2014).https://doi.org/10.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Remote Sensing of Environment148, 42–57 (May 2014).https://doi.org/10

Reference 24

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This paper cites In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Reference 25

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Observation 97ed9042-ef5b-49a3-95dc-d547d9a804b3 · outbound

This paper cites arXiv preprint arXiv:2602.21421 (2026),https://arxiv.org/ abs/2602.21421.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps arXiv preprint arXiv:2602.21421 (2026),https://arxiv.org/ abs/2602.21421

Reference 26

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This paper cites arXiv preprint arXiv:2406.01076 (2024),https://arxiv.org/abs/2406.01076.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps arXiv preprint arXiv:2406.01076 (2024),https://arxiv.org/abs/2406.01076

Reference 27

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This paper cites arXiv preprint arXiv:2501.19328 (2025),https://arxiv.org/abs/2501.19328.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps arXiv preprint arXiv:2501.19328 (2025),https://arxiv.org/abs/2501.19328

Reference 28

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Unresolved cited work

Reference 29

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This paper cites Ecography40(8), 913–929 (Mar 2017).https://doi.org/10.1111/ecog.02881,http://dx.doi.org/10.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Ecography40(8), 913–929 (Mar 2017).https://doi.org/10.1111/ecog.02881,http://dx.doi.org/10

Reference 30

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps In: Advances in Neural Information Processing Systems

Reference 31

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps ICCV (2021)

Reference 32

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Observation 40c1e15d-dc08-4f97-abed-ef9294b68ede · outbound

This paper cites Remote sensing of environment64(3), 331–344 (1998).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Remote sensing of environment64(3), 331–344 (1998)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:41.772806Z

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Observation 0860ca15-9cfa-4777-b20a-75db11d615a5 · outbound

This paper cites Remote Sensing of Environment231, 111199 (Sep 2019).https: //doi.org/10.1016/j.rse.2019.05.018,http://dx.doi.org/10.1016/j.rse.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Remote Sensing of Environment231, 111199 (Sep 2019).https: //doi.org/10.1016/j.rse.2019.05.018,http://dx.doi.org/10.1016/j.rse

Reference 34

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This paper cites ACM Transactions on Spatial Algorithms and Systems11(4), 1–28 (Aug 2025).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps ACM Transactions on Spatial Algorithms and Systems11(4), 1–28 (Aug 2025)

Reference 35

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Observation 92545e25-3f84-4746-acef-b7f935cff04d · outbound

This paper cites Remote Sensing of Environment300, 113888 (Jan 2024).https://doi.org/10.1016/j.rse.2023.113888,http://dx.doi.org/10.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Remote Sensing of Environment300, 113888 (Jan 2024).https://doi.org/10.1016/j.rse.2023.113888,http://dx.doi.org/10

Reference 36

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This paper cites Remote Sensing of Environment324, 114714 (2025).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Remote Sensing of Environment324, 114714 (2025)

Reference 37

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This paper cites Springer New York, NY, 1 edn.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Springer New York, NY, 1 edn

Reference 38

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This paper cites Methods in Ecology and Evolution14, 1320–1332 (2023).

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Methods in Ecology and Evolution14, 1320–1332 (2023)

Reference 39

Resolution: verified exact; doi, observed 2026-08-15T15:29:40.587214Z

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Observation 450bffcf-9033-4af9-8e37-d7d950408f25 · outbound

This paper cites Scientific Data3(1) (Mar 2016).https://doi.org/10.1038/sdata.2016.18, http://dx.doi.org/10.1038/sdata.2016.18.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Scientific Data3(1) (Mar 2016).https://doi.org/10.1038/sdata.2016.18, http://dx.doi.org/10.1038/sdata.2016.18

Reference 40

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Observation 1d7e20b4-ffce-4519-8b3e-3dd2d984aea6 · outbound

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Making Convolutional Networks Shift-Invariant Again

Reference 41

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Reference 42

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Observation 46fa9f94-9366-4afc-a141-5fc1865598d8 · outbound

This paper cites cosine)•Smooth transitions •Blurs predictions in overlap zone •Complicates uncertainty propagation (b) Padding + crop to center.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps cosine)•Smooth transitions •Blurs predictions in overlap zone •Complicates uncertainty propagation (b) Padding + crop to center

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:29:41.691951Z

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From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps Unresolved cited work

Reference 44

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Observation 02254ca7-ead0-40be-a16c-70b3583c000f · outbound

This paper cites 3:Two strategies for mitigating patch artifacts: (a) overlapping patches with weighted blending and (b) padding with center cropping.

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps 3:Two strategies for mitigating patch artifacts: (a) overlapping patches with weighted blending and (b) padding with center cropping

Reference 45

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Pith citing papers

No inbound Pith citation observations are available.