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Paper Citation Record · LEDGER

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

As of 10 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 6 inbound Pith citation observations for arXiv:2507.09081.

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

pith.paper-citation-record.v1
2507.09081 v1

Coverage vector

measured 100 of 103 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:10:19.555298Z

measured 106 of 106 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:53:14.651284Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T23:59:06.525645Z

Reference resolution

100 of 103 outbound references displayed

  • verified exact0
  • verified fuzzy67
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b5b2388-7d27-46df-90b9-52f6bd11e76f · outbound

This paper cites Advanced Science 10(26), 2302361 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Advanced Science 10(26), 2302361 (2023)

Reference 1

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Observation 112186f8-77fa-42f1-aab2-6ffddbab79fa · outbound

This paper cites Nature Reviews Electrical Engineering 1(4), 251–263 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Nature Reviews Electrical Engineering 1(4), 251–263 (2024)

Reference 2

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source=pdf_text observed=2026-08-06T18:10:13.280087Z digest=sha256:03febd66ec7b4451ad3e132bdc819cbcf0f8169c8fa95772585c56279ba56b42

Observation 43b50610-a92a-45b2-a21e-73a4cde52991 · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing 218, 20–49 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion ISPRS Journal of Photogrammetry and Remote Sensing 218, 20–49 (2024)

Reference 3

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source=pdf_text observed=2026-08-06T18:10:13.313491Z digest=sha256:4531f68fd5e9a42e5d28e3ec428fa7bb1cf6940e27071986438140ff4202d9aa

Observation e2aa01ee-9d54-42b8-8c49-d2430fc6dd70 · outbound

This paper cites International Journal of Applied Earth Observation and Geoinformation 133, 104123 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion International Journal of Applied Earth Observation and Geoinformation 133, 104123 (2024)

Reference 4

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source=pdf_text observed=2026-08-06T18:10:13.354387Z digest=sha256:fda6172945a54d84fc0078fe9fa67c92842a3865e214d8d398eaf331fa7ce9ad

Observation 806a75b4-7034-401b-b64a-52207e73340b · outbound

This paper cites Remote Sensing of Environment 280, 113195 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 280, 113195 (2022)

Reference 5

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source=pdf_text observed=2026-08-06T18:10:13.446849Z digest=sha256:6d27b2de3f5f5e6937788fcfc92072e8127fcf3491399ed3474d029cd86814d0

Observation aaa9189c-7cd7-4838-bb9c-cd4ae7eaea7f · outbound

This paper cites Information Fusion 90, 185–217 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Information Fusion 90, 185–217 (2023)

Reference 6

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source=pdf_text observed=2026-08-06T18:10:13.574109Z digest=sha256:756021ca0db1b2bb1e5c42bf11222837dc93972c55a4bc1dca8d133c1f7ca8b4

Observation 75351c36-61b3-4b1b-b477-f50369c44b2c · outbound

This paper cites Nature Reviews Earth & Environment 4(5), 319–332 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Nature Reviews Earth & Environment 4(5), 319–332 (2023)

Reference 7

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source=pdf_text observed=2026-08-06T18:10:13.652459Z digest=sha256:582a45e731a9df1b4342fa728dc6b3b9a1838eebd51b7340dfad2a2bd65ce54f

Observation a3b24e95-dc1a-4424-8f03-71715be97735 · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing 202, 87–113 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion ISPRS Journal of Photogrammetry and Remote Sensing 202, 87–113 (2023)

Reference 8

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no resolver link, observed 2026-08-06T18:10:13.711168Z

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source=pdf_text observed=2026-08-06T18:10:13.711168Z digest=sha256:b9121afead742cdfcff6f604efc3fa35924898800d03f9ee94cca5b5169266c1

Observation 04f9a4bb-fec3-403b-bd67-8d15f1dd3d95 · outbound

This paper cites National Remote Sensing Bulletin 26(2), 268–285 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion National Remote Sensing Bulletin 26(2), 268–285 (2022)

Reference 9

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source=pdf_text observed=2026-08-06T18:10:13.777072Z digest=sha256:72e07df383417c7092716673ff50c3de36b4ed470761f7fcb02ff2481fb763ba

Observation 9672cdeb-8eef-4200-867b-b6a6e736de33 · outbound

This paper cites Artificial Intelligence Review 57(9), 224 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Artificial Intelligence Review 57(9), 224 (2024)

Reference 10

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source=pdf_text observed=2026-08-06T18:10:13.937530Z digest=sha256:278380bd5132909c3523169bd22be9c19a0634df81d582f56c58bcf5224e4b56

Observation 61145281-5f00-40a9-b5b0-70dfc250af1b · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing (2024)

Reference 11

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source=pdf_text observed=2026-08-06T18:10:13.987305Z digest=sha256:12717bc34dc7fd3545e0fdf75009f2b87ea25bde45b3377c17473e1800668a6d

Observation 11d8ad11-dde1-4560-aede-79e57cae6c54 · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing (2024)

Reference 12

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no resolver link, observed 2026-08-06T18:10:14.101972Z

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source=pdf_text observed=2026-08-06T18:10:14.101972Z digest=sha256:3e605c07675ae5c56d855e031edcfcdae73c9c262d9fc74d0b72d994a30d05a1

Observation 9f3f1171-c25a-427f-994b-bb943e4754f1 · outbound

This paper cites Computers and Electronics in Agriculture 223, 109111 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and Electronics in Agriculture 223, 109111 (2024)

Reference 13

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Observation dafa607c-ac15-42c1-a052-d63fb841635a · outbound

This paper cites Computers and Electronics in Agriculture 221, 109017 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and Electronics in Agriculture 221, 109017 (2024)

Reference 14

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source=pdf_text observed=2026-08-06T18:10:14.257340Z digest=sha256:20b2bba31be3ea252b56d72672d93a167e60b5f01487d927c7091406b604511e

Observation 8313290a-48ec-42dd-8222-7892cd6cb52f · outbound

This paper cites Remote Sensing 16(13), 2401 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing 16(13), 2401 (2024)

Reference 15

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source=pdf_text observed=2026-08-06T18:10:14.299090Z digest=sha256:339d358552448c2f68ec37e1b27eb48ae3fc8e3dce02eeb206fa65d22ee3fd12

Observation 645379a1-c7d2-4914-b4f4-b8f10a420f99 · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing 62, 1–13 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing 62, 1–13 (2024)

Reference 16

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source=pdf_text observed=2026-08-06T18:10:14.340790Z digest=sha256:09e8855eb9902a9067f81be90ebbdb65f48b0e40d2dcaf28798438e82c9721cf

Observation 53618937-fdf1-4dcc-bc7c-404a04df15fb · outbound

This paper cites Remote Sensing of Environment 311, 114308 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 311, 114308 (2024)

Reference 17

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no resolver link, observed 2026-08-06T18:10:14.368102Z

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source=pdf_text observed=2026-08-06T18:10:14.368102Z digest=sha256:0f45dbb236e7a51b683de419d5c834f6189e21ef082029b1daaf47bbd10944de

Observation 60f0548c-6516-4691-89a9-8dddb64663a5 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

Reference 18

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no resolver link, observed 2026-08-06T18:10:14.417145Z

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source=pdf_text observed=2026-08-06T18:10:14.417145Z digest=sha256:48bc62375c6390fec94b5b3624ab6b5c0a619c66d68abbc9785ab22bf0b5fbef

Observation 3e3cd446-a4e1-4e67-849c-9bed6f959904 · outbound

This paper cites Advances in Neural Information Processing Systems 35, 197– 211 (2022) 25.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Advances in Neural Information Processing Systems 35, 197– 211 (2022) 25

Reference 19

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Observation 83ad82a3-c79d-4898-8562-fb1d9512732c · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Reference 20

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source=pdf_text observed=2026-08-06T18:10:14.508036Z digest=sha256:2e06aac6e6cc480854a6fb8f288fa010894e11dd83f7c056df0bcea0bd18d3aa

Observation 5e037f76-4807-4ff8-a008-cf3c9dc6fcb1 · outbound

This paper cites In: European Conference on Computer Vision, pp.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion In: European Conference on Computer Vision, pp

Reference 21

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source=pdf_text observed=2026-08-06T18:10:14.569962Z digest=sha256:d67625f3930d01dfeb7325e49d8d4e7009511f1c9a2d9c4d0b67129b3191de47

Observation 4205d229-4430-4297-be65-7cfaf718215f · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion On the Opportunities and Risks of Foundation Models

Reference 22

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source=pdf_text observed=2026-08-06T18:10:14.635674Z digest=sha256:652c97535301bbb0f3f738c14d7d0d0f79d126445fddba8bb3475e30a7ea3d97

Observation 0b6f2b49-7901-493f-b89f-ac9cd5a36e6f · outbound

This paper cites IEEE Access (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Access (2025)

Reference 23

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source=pdf_text observed=2026-08-06T18:10:14.681705Z digest=sha256:f69ec64c8b523c58b09357f18e12dd08b92ca32b108e626b9201cbb2b1fa9151

Observation b75d3338-cb7e-4872-9b26-b35527bbe266 · outbound

This paper cites International Journal of Artificial Intelligence for Science (IJAI4S) 1(1) (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion International Journal of Artificial Intelligence for Science (IJAI4S) 1(1) (2025)

Reference 24

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no resolver link, observed 2026-08-06T18:10:14.711490Z

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source=pdf_text observed=2026-08-06T18:10:14.711490Z digest=sha256:6f21ed6b1d7cbcef8bbe2af3aa8ec8223a84f491397c662232d5552d3dd7dd85

Observation c779d063-3052-46a7-af3e-7518da19d930 · outbound

This paper cites Computers and Electronics in Agriculture 220, 108891 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and Electronics in Agriculture 220, 108891 (2024)

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-06T18:11:22.280911Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 86b9f78a-55fe-4101-bec8-c260745c33f6 · outbound

This paper cites Journal of Hydrology: Regional Studies 52, 101689 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Journal of Hydrology: Regional Studies 52, 101689 (2024)

Reference 26

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raw_fallback, observed 2026-08-06T18:11:22.207399Z

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Observation 37bfb14a-b507-47ac-9734-1551cd0a17dd · outbound

This paper cites Remote Sensing of Environment 307, 114123 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 307, 114123 (2024)

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-06T18:11:22.125175Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:14.842060Z digest=sha256:7b31ccaaee20448bb2acad14d3c3de442f4bca29f49d2a2670f4d26741894b23

Observation 1bff864a-161e-46fe-8634-11570c44d00a · outbound

This paper cites Ecological Indicators 159, 111653 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Ecological Indicators 159, 111653 (2024)

Reference 28

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raw_fallback, observed 2026-08-06T18:11:22.064240Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0f0f4343-6e78-44bb-879b-5c5a9fa178d0 · outbound

This paper cites Remote Sensing of Environment 309, 114226 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 309, 114226 (2024)

Reference 29

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raw_fallback, observed 2026-08-06T18:11:22.001339Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 089e8ae6-4552-4944-8592-067c5cc13e4c · outbound

This paper cites Geoderma 398, 115118 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Geoderma 398, 115118 (2021)

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-06T18:11:21.889136Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:14.919394Z digest=sha256:cdc5e74cfc9d10d3f29348d88403dddf8ab9a9d5fb48d7610e5165506f482299

Observation ebda15f8-3cce-4ec3-b2f6-12c2fc753148 · outbound

This paper cites Remote Sensing of Environment 304, 114066 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 304, 114066 (2024)

Reference 31

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raw_fallback, observed 2026-08-06T18:11:21.754087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:14.950942Z digest=sha256:486f20fa05d8ff4967ef908c628c0831c2f1881409c415bb474f2b2fa0852ce3

Observation fb366cbb-78c4-426c-87e4-a973e4190538 · outbound

This paper cites Computers and Electronics in Agriculture 218, 108686 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and Electronics in Agriculture 218, 108686 (2024)

Reference 32

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raw_fallback, observed 2026-08-06T18:11:21.656652Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:14.992317Z digest=sha256:df1338d77ff6c451e9fe2ec9b6e995535afdcb8743155f56d4815a03da73477c

Observation ae26cd4d-1a3e-4f1a-a714-72fb132b8423 · outbound

This paper cites Nature Communications 15(1), 7816 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Nature Communications 15(1), 7816 (2024)

Reference 33

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raw_fallback, observed 2026-08-06T18:11:21.589758Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0d7e7b78-8163-4608-abf4-24b2777aac9d · outbound

This paper cites Applied Sciences 14(14), 5980 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Applied Sciences 14(14), 5980 (2024)

Reference 34

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raw_fallback, observed 2026-08-06T18:11:21.517308Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8d24000d-cc01-48aa-9519-a04d22649adc · outbound

This paper cites Remote Sensing of Environment 301, 113943 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 301, 113943 (2024)

Reference 35

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raw_fallback, observed 2026-08-06T18:11:21.444913Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d6165957-ddee-4563-a7d2-2d9d2a1648c7 · outbound

This paper cites Nature Geoscience 17(6), 539–544 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Nature Geoscience 17(6), 539–544 (2024)

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:21.382386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.165031Z digest=sha256:a228ef380a58ca5556a623282fc9a756c7092236804a9fd45d40867a3e37544f

Observation fd2ba2f6-0c71-49fa-903b-d950c71abd9f · outbound

This paper cites Rainy: Unlocking Satellite Calibration for Deep Learning in Precipitation.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Rainy: Unlocking Satellite Calibration for Deep Learning in Precipitation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.203628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.203628Z digest=sha256:0c8434bccf5776542897c85b65af6e095fd1c3abb0f3ee88881c816d3501f838

Observation 506b2395-650b-4ba5-950b-a536ce612d02 · outbound

This paper cites Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery

Reference 38

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T18:11:14.499815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.243935Z digest=sha256:dffd92c6fbeea8e6813aa4c166994d783ff6e6c4bc8bc3c488bfe02e1d9a1710

Observation 783be4bb-26d4-468c-a4ae-f8050b2c199a · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing (2025)

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:21.309562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.288037Z digest=sha256:67b48312e68dc3d4a9825e8d0c8fcba77f05e720ff7fbc56fda443b316923859

Observation 93dc1fca-6a7e-4361-b107-d6e65bec0d57 · outbound

This paper cites Pattern Recognition 164, 111579 (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Pattern Recognition 164, 111579 (2025)

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:21.241237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.323391Z digest=sha256:669db7cd0711a50b757c89a9e073ec4137e3109181ccaf3eee444871e9f00182

Observation 35497942-893f-4316-8e8c-9610379e299c · outbound

This paper cites IEEE Transactions on Multimedia (2025) 27.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Multimedia (2025) 27

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:21.147425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.363404Z digest=sha256:9922bd21f98d67b368e4452d9a815b914f6b89ce068ff6a3fcec708bf61fe63b

Observation 04a94c4d-b9bc-4f1d-ba80-8fae0dbe8338 · outbound

This paper cites A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.402040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.402040Z digest=sha256:940b7cd2506782f725c97dc288c92ec0386bc66a5160eed0a86272816bc5661b

Observation 8da0dd0d-4022-40c3-ad66-5998442770e0 · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing 108, 273–290 (2015).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion ISPRS Journal of Photogrammetry and Remote Sensing 108, 273–290 (2015)

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:21.012261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.438776Z digest=sha256:1298ece4a00388d7d89082318ba7928c393a3eeebd262a2d87ef5fd58b981caf

Observation cf76fae7-2144-4715-bbb8-496c072d7bac · outbound

This paper cites SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.476685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.476685Z digest=sha256:8cf844562c2280474f909f35a0802d5321f48962684bafc5c10ec21f1a3791a4

Observation b5a448c8-e247-422a-9421-b65616cdf569 · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing 60, 1–11 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing 60, 1–11 (2021)

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.870261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.513629Z digest=sha256:8447d3d5ff54d3b2d0be6b3b161764715ab56f805b61126bfd1908be69530704

Observation 4605291e-12e2-43f1-8373-b479187bf136 · outbound

This paper cites IEEE Transactions on Circuits and Systems for Video Technology (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Circuits and Systems for Video Technology (2025)

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.777166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.572827Z digest=sha256:74618cd626be1728943da8d64905d3df09f990b81d15593959efddeb659dcb14

Observation 5e5d3509-92e5-47b6-9bff-3496cc9138d4 · outbound

This paper cites Remote Sensing of Environment 306, 114117 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 306, 114117 (2024)

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.668017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.605720Z digest=sha256:dc19ef7f92ca7efb0437b2012074fb17be310964df959506a155bcc8c3c6f71a

Observation bfc56109-4620-4b2c-8039-c48bd396a8b2 · outbound

This paper cites Computers and Electronics in Agriculture 225, 109238 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and Electronics in Agriculture 225, 109238 (2024)

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.540501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.697705Z digest=sha256:1a524a1087afd5e0f425d5a162a5be28f843de69f0b031325f5cb586a3ae776d

Observation 7c5f876b-47db-43bc-95cc-55445b754830 · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing 198, 297–309 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion ISPRS Journal of Photogrammetry and Remote Sensing 198, 297–309 (2023)

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.450394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.807846Z digest=sha256:7f873d41a1ee78cf883dc207d77e49373472e7d11c2931462db07c4e3333a228

Observation 94058b4a-25ae-4406-aa46-afb9e8f501aa · outbound

This paper cites DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.836524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.836524Z digest=sha256:b73d828b9924842d23854e7d4b8f994602c101cf027240b9b14f8b3375391a5f

Observation a81f63e2-971b-48fb-82fe-b5876ca64619 · outbound

This paper cites SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.899510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.899510Z digest=sha256:292be029ccac261621bba7ee9b834cf0e4e407e99148481adc647f19cfdb5872

Observation 54fbfb61-8615-4afe-a094-061073403765 · outbound

This paper cites Remote Sensing of Environment 286, 113421 (2023) 28.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 286, 113421 (2023) 28

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.291821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:15.977368Z digest=sha256:7d6bb83d35f59dd0de32aca60fd45fee5e26cfbf65b51fa910b0d892eed97b6b

Observation 9a5e65ae-b693-42da-a2ba-ad2936992a5b · outbound

This paper cites 0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion 0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.197364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.030474Z digest=sha256:3f15d9d45fd456518e494691c1a01c3041e8e1023f04e72882afbd67c0ee1112

Observation b0b982b6-582a-4255-a5f0-2e680f623385 · outbound

This paper cites IEEE geoscience and remote sensing magazine 5(4), 8–36 (2017).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE geoscience and remote sensing magazine 5(4), 8–36 (2017)

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:20.038717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.118455Z digest=sha256:16467c6a6844e6dd482596232594b99208827586aea97e404f06fe9cbe5c4dfb

Observation 3da3c964-931e-4598-b507-6ee0c6862644 · outbound

This paper cites IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 9842–9859 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 9842–9859 (2022)

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:19.844964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.219874Z digest=sha256:fdf2351abaf15e111081a61e635cd88764660a59ed50bd44dd39bbdb8e6b5c0e

Observation ad1704ff-64ed-4246-92bc-8236b8ce0212 · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing (2025)

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:19.698441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.263373Z digest=sha256:ecc4883a44afeab582a80444486b80daf2373dd129b0057ca20b9da45d25a513

Observation 04b00904-ffe9-44b8-a39c-c0a558f313f7 · outbound

This paper cites Remote sensing of environment 113, 56–66 (2009).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote sensing of environment 113, 56–66 (2009)

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:19.570859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.347010Z digest=sha256:20f067420acc09cb33c688653c900fe64efd1f7433a0a2fdc541a1bf34e6a6d8

Observation a737ec4a-8f74-4c6c-afa2-dac8e34ccef5 · outbound

This paper cites International journal of remote sensing 25(1), 73–96 (2004).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion International journal of remote sensing 25(1), 73–96 (2004)

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:19.438190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.421508Z digest=sha256:4067f75443b6a2ed689a090b1810353687269c4725bf61831af7c40a45be332e

Observation 118a7cda-5444-4f3f-ba73-6f5f1152e5ea · outbound

This paper cites Geoscientific Model Development 14(7), 4697–4712 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Geoscientific Model Development 14(7), 4697–4712 (2021)

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:19.287092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.494898Z digest=sha256:aa8cb9befeb396f7f8e016dc16e34636d23f825e029d852e12c409dbf3c6f696

Observation b2132a81-5e4d-4e07-adef-aeebd11ae05b · outbound

This paper cites JSTOR (1995).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion JSTOR (1995)

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:19.139773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.572370Z digest=sha256:b62776c7a2742a23db80838eebc23d81d3ac85a2c71142bf40d75455a2c8322d

Observation a416c3a2-67e4-494c-a45d-b19ef3e6e6d1 · outbound

This paper cites Remote sensing of environment 112(4), 1702–1711 (2008).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote sensing of environment 112(4), 1702–1711 (2008)

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.995369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.687009Z digest=sha256:af868ea91fce2d7190580958c11b9e62634e0fb12ea7d35d0cc670746f7184a7

Observation 86d37f67-a74b-4531-8d6a-8f0c3a44d3b1 · outbound

This paper cites IEEE transactions on geoscience and remote sensing 35(3), 675–686 (1997).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE transactions on geoscience and remote sensing 35(3), 675–686 (1997)

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.885205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.758269Z digest=sha256:28e77252f245a6d7346ffbe63cce8956ce03059637ff3dee35140c10c0f8bbd6

Observation 80910e99-a25e-478b-b632-6b4b6a895b16 · outbound

This paper cites Remote sensing of environment 87(1), 23–41 (2003).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote sensing of environment 87(1), 23–41 (2003)

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.813553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.795962Z digest=sha256:dcfd3f574eeaee743c9933098a2472b3ec64edf2ca190e494d874b0e0dee7ab7

Observation 03a91f4f-58b6-465a-84a1-e62cfe685fab · outbound

This paper cites Remote Sensing 14(5), 1114 (2022) 29.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing 14(5), 1114 (2022) 29

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.740148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.873056Z digest=sha256:ba5753c8957225a120d461bd6720f7065e5dc16950d13401dc551ade03141f31

Observation f73f7b7b-2c6e-48c9-beb3-2f30d202947b · outbound

This paper cites IEEE Transactions on geoscience and remote sensing 34(4), 946–956 (2002).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on geoscience and remote sensing 34(4), 946–956 (2002)

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.641680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:16.930013Z digest=sha256:d64b9405c2c29f7d872a092339a86799da26886ecdef5933911ca0a1a930b0b2

Observation 223a0da2-1d37-40b3-9684-e1f6e1675d25 · outbound

This paper cites Remote Sensing 11(19), 2283 (2019).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing 11(19), 2283 (2019)

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.534007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.000374Z digest=sha256:6a96b9e8d355f3aecd57d11097a2d36ed57b9fdbde7490562cdcb7482c72e72d

Observation 4e226097-f316-474e-995f-2fe7deea136a · outbound

This paper cites Remote sensing of environment 84(1), 1–15 (2003).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote sensing of environment 84(1), 1–15 (2003)

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.441006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.153728Z digest=sha256:253498a8d599d3e93a2c6a2b84f9aaad965f7b3cf0434539179b593b686ee836

Observation 61b811e8-3c79-4d37-8b0a-468da566f812 · outbound

This paper cites IEEE Transactions on knowledge and data engineering 29(10), 2318–2331 (2017).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on knowledge and data engineering 29(10), 2318–2331 (2017)

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.296037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.252315Z digest=sha256:78a24b4b40620516b1bc1b911c62b89f44f092cf07eaee7c94ccb6d07c6be532

Observation c7141e8d-9f5b-43c4-b273-cfbdebf0cbdc · outbound

This paper cites Remote Sensing of Environment 261, 112476 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 261, 112476 (2021)

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.222016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.345170Z digest=sha256:778a5e03a90a164743a0c98b982281618fc664905246acd52201cc2a1b3d9c4c

Observation 3aaae55c-7774-47db-88b3-bfedf2e8c666 · outbound

This paper cites ISPRS journal of photogrammetry and remote sensing 114, 24–31 (2016).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion ISPRS journal of photogrammetry and remote sensing 114, 24–31 (2016)

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:18.077713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.395538Z digest=sha256:fb36cfd6d3706f4a864b031adebc3c720e3f4992a0192b6dddc49a193f051990

Observation 749e6b38-bf9c-4577-bcd2-444a3e2d5d3d · outbound

This paper cites Computers and Electronics in Agriculture 200, 107130 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and Electronics in Agriculture 200, 107130 (2022)

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.931166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.469117Z digest=sha256:38639afc59ab9f3dc7b03580d2c2e9dad6c3ab21c6835654e48c28b60ddd9639

Observation ee75ab54-9e07-41ad-9cba-1ee70cf03bc9 · outbound

This paper cites 5 using fine-resolution satellite data in the megacity of beijing, china.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion 5 using fine-resolution satellite data in the megacity of beijing, china

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.820094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.572145Z digest=sha256:77c7b5221f8eeddf7fa62c2363538100b1bbcb80b7bc9e54fda1196582f448c1

Observation 90cbb4ec-e051-40e0-945f-3e58e1a75e86 · outbound

This paper cites Remote Sensing of Environment 311, 114317 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 311, 114317 (2024)

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.686181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.662369Z digest=sha256:e625f3176f4ed9002a6dd7bcfd5207a8c8f0494ce6a23ddae8e9426d8e4e7fe4

Observation 566220e9-b91a-486c-aa65-50de8b48cbaf · outbound

This paper cites IEEE Transactions on Fuzzy Systems (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Fuzzy Systems (2024)

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.550714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.725661Z digest=sha256:6fab0535ebc10f3a7a0f58593b8fc53c490606298a28f0ed03264abda9b48884

Observation 9873b1ee-6880-4ec5-84ef-93cb49e2ef01 · outbound

This paper cites International Journal of Applied Earth 30 Observation and Geoinformation 114, 103060 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion International Journal of Applied Earth 30 Observation and Geoinformation 114, 103060 (2022)

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.428151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.781368Z digest=sha256:2280f5c977bc4a6f55c7b8d5ccc065a078cc787d41fa435316e28f2eac84e06b

Observation a6dd5e97-a056-4aa6-ab47-ea85ab088328 · outbound

This paper cites Journal of Hydrology: Regional Studies 56, 102023 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Journal of Hydrology: Regional Studies 56, 102023 (2024)

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.275731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:17.880052Z digest=sha256:6ffe3ba8a24c73b6cd16378974d212bf4f5dfa8ba4c9b4e469505e1d95e87d59

Observation 7c830c14-2f4c-4280-9da7-070313497fa8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:17.962292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:17.962292Z digest=sha256:ff544e989cb0a2fb1d8eac15de7e1ed3b82845c837c607016c3298aa0cbb3056

Observation 9573a203-d032-482f-9b24-6f7617acff01 · outbound

This paper cites Artificial Intelligence Review 58(9), 272 (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Artificial Intelligence Review 58(9), 272 (2025)

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.163282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.023378Z digest=sha256:858e703d5facdecd292bebc39d946ffc0b35a8ec8ce78addce9779dec8132d82

Observation 10988692-ddd7-45f2-9f07-5216d18d6437 · outbound

This paper cites International Journal of Remote Sensing 43(2), 630–648 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion International Journal of Remote Sensing 43(2), 630–648 (2022)

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:17.129961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.078357Z digest=sha256:ca57bd4cba36b53dc9373a6c4cd39c53c7dd1346074d4223df51066b43697612

Observation 236c2436-157e-4fd2-9d44-afd505157e6f · outbound

This paper cites an unresolved cited work.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Unresolved cited work

Reference 80

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:11:16.999285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.153616Z digest=sha256:0778da2915ef99d7b09e601cd39ea87d8606af70584500ae84c10c2f9acca20d

Observation a4803f3d-9b42-4274-a270-e9856d5a47bc · outbound

This paper cites Neural Computing and Applications 37(5), 3809–3826 (2025).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Neural Computing and Applications 37(5), 3809–3826 (2025)

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.907756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.238359Z digest=sha256:65fd8ceadb8973f17c31c801638d315e0f207d4a06a7e29180effd971bbac590

Observation 91bf5d98-9680-499b-a322-19125f899d46 · outbound

This paper cites Remote Sensing of Environment 310, 114241 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing of Environment 310, 114241 (2024)

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.801949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.299350Z digest=sha256:0cba262b45fbf189b131edf96431cf5bc5f7f7ad5efbe70903989e7f1d209c00

Observation d14ad2a0-a948-4316-a1bb-2819181f1941 · outbound

This paper cites International Journal of Remote Sensing 45(19-20), 7753– 7774 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion International Journal of Remote Sensing 45(19-20), 7753– 7774 (2024)

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.659689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.334983Z digest=sha256:e6c2dff2e8e559340d8129524d41531d6f9c6fa62e4cfd6b0070ac36f8572ad7

Observation 6b1e4986-fc70-4b1b-abc9-465af9fd3fc4 · outbound

This paper cites IEEE Geoscience and Remote Sensing Letters 19, 1–5 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Geoscience and Remote Sensing Letters 19, 1–5 (2021)

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.472760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.410260Z digest=sha256:81adda369e5fec235035b0014d9723cea3ca3831dc2a5398c0b9d900ddeb6acb

Observation 4f4a769d-a817-4a98-a4e5-3ed22b7988bd · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing 60, 1–11 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Transactions on Geoscience and Remote Sensing 60, 1–11 (2021)

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.353580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.492329Z digest=sha256:04afe54ab214f9defdbe68e1139faa335f00f4c22bc15f0d42f73eeee37e47a5

Observation 989067ce-32f5-4333-8304-6369aee384f2 · outbound

This paper cites Journal of Hydrology 635, 131203 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Journal of Hydrology 635, 131203 (2024)

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.252560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.601416Z digest=sha256:f3ff5ccd1aebd1374a71192e4134aca700842d7b9b1b76e7d96679d7a9a93b7f

Observation 616ef118-5be7-46e2-bed5-39f5036dd005 · outbound

This paper cites Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images

Reference 87

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T18:11:14.242156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.709075Z digest=sha256:5f4a28499261c6c69b81fd453c3b7c9377c0f6e4671827d1a9608b85c3b7e18b

Observation 2a82e838-3709-4b8e-976b-9d9f45735aaa · outbound

This paper cites Remote sensing of environment 221, 635–649 (2019).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote sensing of environment 221, 635–649 (2019)

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:16.089371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.785536Z digest=sha256:17546c21d552cac15231d2994be1848f7b7239af831f17130730f9be3d986a83

Observation abbcb259-b29e-4085-83b2-ee6ff71be327 · outbound

This paper cites Remote Sensing 15(14), 3534 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Remote Sensing 15(14), 3534 (2023)

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.938328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.861785Z digest=sha256:60d1b19755db620b3ae3de5aa32947be2193c411f8658937bc1027e49db8e161

Observation 72838156-ad54-4d63-a940-2ecd53771de6 · outbound

This paper cites GIScience & Remote Sensing 61(1), 2393489 (2024).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion GIScience & Remote Sensing 61(1), 2393489 (2024)

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.841974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:18.950980Z digest=sha256:b62e0c78403cf253778b94159874935eb9e277f553fcc543f6d981127cb5b56b

Observation a6eeac43-5746-445e-b7a3-a270519e5ccb · outbound

This paper cites Water Research 229, 119478 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Water Research 229, 119478 (2023)

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.717336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.022885Z digest=sha256:e144dd3d5b5f320aa542399098b8ce87e7ab78dc64aa1eb41a291f5db11a99fe

Observation 07b02ea0-d985-48f2-814d-d4c86aa5fe10 · outbound

This paper cites Computers and electronics in agriculture 190, 106480 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Computers and electronics in agriculture 190, 106480 (2021)

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.594210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.092651Z digest=sha256:187dc93ccf23e6096ebe7054d4d11fd725ce683c481655a23ee9589d7ff334f1

Observation 175106d6-12da-4678-9d55-9e2b778cb3ea · outbound

This paper cites IEEE Geoscience and Remote Sensing Magazine 9(3), 174–180 (2021).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Geoscience and Remote Sensing Magazine 9(3), 174–180 (2021)

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.545205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.131180Z digest=sha256:2d7a74f63ae06e1ce7382ef11d6120a5b39170ac8f4e1b172066da920d1f40a1

Observation 14fb082a-6355-459b-a681-64fe3d49c683 · outbound

This paper cites IEEE Geoscience and Remote Sensing Magazine 11(3), 98–106 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion IEEE Geoscience and Remote Sensing Magazine 11(3), 98–106 (2023)

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.490116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.164735Z digest=sha256:62226e5f1d7b06d2e634de16b41c2680fbeed9b0f5a5d2dc8c240bd1b12281ac

Observation 629aa047-c6e6-4805-bc81-42cf207df45d · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.379957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.228463Z digest=sha256:d2c72db1cda9550222880421fd22c672848390bee9ab1a4c5800544954e5c6fa

Observation 9a2ff694-174f-4c2b-8072-1e7f4511e3b1 · outbound

This paper cites In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.296797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.312064Z digest=sha256:fd85e84cbb60a0ff39b087a21ea12ecd5b11936aa97eea5e588de8c10a37e4d5

Observation d27d0955-36d3-469e-8b47-61db7b927208 · outbound

This paper cites In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp.

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.178441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.413664Z digest=sha256:88508623bccfec21b3f7945d53cadfb0d06169925692c53530859192037453da

Observation 6b731f29-b1ae-496d-bfd0-7b9cb72a16ec · outbound

This paper cites Advances in Neural Information Processing Systems 36, 59787–59807 (2023).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Advances in Neural Information Processing Systems 36, 59787–59807 (2023)

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:15.081130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.444663Z digest=sha256:e828e014201ebfc786f601c32ef9e4bd92e7672cc42f0369bf8521f5b045359c

Observation 70aee07a-b54d-45ca-bbce-f552e2f2ea40 · outbound

This paper cites Pattern Recognition (6) (2014).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Pattern Recognition (6) (2014)

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:14.979671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.499058Z digest=sha256:28a5232fc6dc91bbb5a5036b195a29d3cb7f3f9da937aba5be49dd682c3bd41a

Observation 9fbc0013-b4d0-4216-9274-fef804d8b4ed · outbound

This paper cites Geoscientific Model Development Discussions 2022, 1–10 (2022).

From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion Geoscientific Model Development Discussions 2022, 1–10 (2022)

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:11:14.918895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T18:10:19.555298Z digest=sha256:762864ee334da6348208d5d91d98a6b6a7f55e562225cd9002f2425aef09bde9

Pith citing papers

Observation 145511d8-7445-4645-a5e3-c39f13a0d687 · inbound

FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving cites this paper.

FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-15T19:19:42.818479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T19:19:42.748573Z digest=sha256:9709250a42968a77acefcb05ecbb1f7c960f2abb8feff2c935457f6d28eff858

Observation c74da11c-9c57-41f3-9e81-20f6c8cb0d88 · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:01.572627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:53:01.572627Z digest=sha256:874396a575ac0baf575f6238130bf92381b5275831df2bf4102c7fa80a3507d0

Observation cf67b0c0-64ef-46cd-8fbe-0f950ec4d3ac · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:14.651284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:53:14.651284Z digest=sha256:16fd96ff9dbd37173475652a7435d44310c3ba5744738e3383bd74286fa1d87c

Observation f5b3566b-d77b-4fe9-a1c1-298da717bb3e · inbound

Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages cites this paper.

Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-07-01T20:56:14.041033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T17:36:28.436212Z digest=sha256:de897d48fbfe07999d6fd01b613f1ad745701c44dfa1689768c933d91c019fbf

Observation e720ce4d-950e-4f97-a483-3d46118fe8bc · inbound

URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification cites this paper.

URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:59:06.528170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T21:37:08.441371Z digest=sha256:3f270f226ec023d3f383b9110b50f1989ee3a1c6aae745da640fe68516a4acbe

Observation 88d17aa1-fcac-4b2b-a331-6fc4964854f0 · inbound

Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation cites this paper.

Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-07-01T08:35:34.278591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-01T07:19:36.472441Z digest=sha256:0e4d87cc3118300be9636afba0ad3cee8a42665adb94deb7df61336f1ae8e5d1