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Paper Citation Record · LEDGER

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

As of 14 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 2026-08-14T06:32:32.682623+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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no resolver link, observed 2026-08-06T18:10:13.446849Z

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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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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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:14.749769Z digest=sha256:6fdb4e8412fb73f1695a05c9cf45f592d43b60e5842595629f2140ff88730f27

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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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:14.842060Z digest=sha256:05d9570331b6f37222faa7fc4c18ac6105e76fb57563b7063cf61188939b2d61

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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:14.865476Z digest=sha256:260df0b2d22db3f647724615c4ba25152b6cc711ee2f3df72a904caba1f5f5f8

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:14.887835Z digest=sha256:d0b43c1eae3a279e53b19d76859db812ea62ae862aae9f514f2cc72dc96ff8d9

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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source=pdf_text observed=2026-08-06T18:10:14.919394Z digest=sha256:4639b64d03c7a594380273879854b43002929dd4956b9405d7cfca3224f5d63e

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:14.950942Z digest=sha256:48034540fac60d4a4686488db1d605ab5f7ffb2ff26fc0301a9715b3d3d37116

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:14.992317Z digest=sha256:81d6d98bc310a2a11fa4705ca703513686c9ea48d82ee8d6adec225d21fc61df

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.028826Z digest=sha256:1b8ad2bdbe43444481a1eac91c2e48c5ac8b76643737398ffb47f01da80fb917

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.085720Z digest=sha256:b45fd86a495b242f562c198c3c38f85ead44d089530d07f7aac41fa24e56a931

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.115114Z digest=sha256:223b7fafa81264e8b0baaef245e5e3dcc1a6dcfcaf92041d4700cee6b3766e68

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.165031Z digest=sha256:99484b8b7d84bef53a094a7809d443947d373d0f34b3d17b6ac56d8c1715bc7e

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.243935Z digest=sha256:7db3875694864aa2918a7210b891717cd35f62a934adcf8504fd93753430e9a0

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.288037Z digest=sha256:c84b20540faaeb2c4c03ea90658bac25eea719c28086ab5d1763705bb650c41b

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.323391Z digest=sha256:1130d3b26700505d51019a86e1bf7d3473da5f0cc34aa4882425ebe92198183c

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.363404Z digest=sha256:e556989c5c87e63e0229f1631894584220bb093aa10d7a38bf526f58c2d952ad

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.438776Z digest=sha256:e9ea9150797b0aa1d26094f97f1b728efe3a391dcc4db133493c95a891f4927a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.513629Z digest=sha256:be57bef4fb73f7cdfee537c5b039c07d24f346b5baf9bb752e816f6bf25b7a6b

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.572827Z digest=sha256:012bcd147a05fc5e4f5867db3b5c4b8ceb5096885edb65ebb81ad30ae256d1f7

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.605720Z digest=sha256:81be9772021c12fd9385c05ffe6565afc55f52f3877658421b648188330329d7

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.697705Z digest=sha256:f79176b567c0fa9f80b3a053553cc059a7be79c301261e591b026c1ffc264841

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.807846Z digest=sha256:c960fa4011417d4eab8ed5331c2a91857e0fceae2c136f3c2568f152b66e2879

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:b9c03ab31d3e676c88c30c706f4f0822d659da400776bd031a6b2c6033e11ad5

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:15.977368Z digest=sha256:c8f6c5671e57ccce1250e6d2e6c944cd78e4b88fe49f19bd4cfdd76abeec61d2

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.030474Z digest=sha256:e2328772e75241c72059b341e1f9ea37b5e663575f54996becb1dd5df82976e1

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.118455Z digest=sha256:40da509e904f9b48f3b1db5ec84c0d483691a5b0a3ae45f0b3354ce136976f33

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.219874Z digest=sha256:066d11ece412069e56de2ea91260e86f7c1425684a81ddcdc1de56da02e9756b

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.263373Z digest=sha256:c68f739cf5ae8abd40f7792b2ca05a000eb3db20d9393f3111aa09621f198b1b

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.347010Z digest=sha256:55edf233cd658b2b33c97a7262be3358d0a90fa7de44af6487b2a35268368a40

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.421508Z digest=sha256:57821159c01123d65d5bf6f677eb89ca01b8fb576faf9213f329d250f6154c3a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.494898Z digest=sha256:1256c5bea5f7ff448f5edc29c029c27383a4ecdc904f73ecd44f1076472dd877

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.572370Z digest=sha256:093b1e4ebd692746b9bbc0e9a1dd559fad7eb3a1846f85492c106ae3ad21c443

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.687009Z digest=sha256:eb46ee9e8a8ceeac076330dc4083687b86f58b225abcb9d62755c97cfce551cb

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.758269Z digest=sha256:e181593c389d606840e75fa0aef51cd35d261c2e872d14aa534f0bdcdf40215e

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.795962Z digest=sha256:4d918392ea3142c3ab3ada7218d33ae9399bf018f900fe44bdae104e690efcac

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.873056Z digest=sha256:9965ee31c95cfb964a8e62e9a1d980f051779e1fb93bc2986441f81e631fa400

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:16.930013Z digest=sha256:e0737e53177f8796ec8e6144b3385d65acdae1eed0caed176aaef41fdc988847

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.000374Z digest=sha256:2ab5cd8c3138ad073edbbab2023b9f6bd98187bf4e8c39e85b4aeb55f832f391

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.153728Z digest=sha256:d38e00848c90e1cb2aee3222ed0c7b6d4e468b98e07aa395cee3a482218603ce

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.252315Z digest=sha256:b71a468852f4511bc6b543d2b9f0f40ec6d07de7abd2bd3334a37147ff83c8df

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.345170Z digest=sha256:691d76999b82df9de5607cc4fd87c40791807833b218edbbdd10a12e30144aad

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.395538Z digest=sha256:834d1a07df92e302b13d43465fc495fb71cfde045fd76eb3a789d03a35751a32

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.469117Z digest=sha256:b10fdf3966b2edd1b83dcb6363c9e5566c4acc3cd38fc0600a27b739623a9c4d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.572145Z digest=sha256:8ee2d3e7d233ccfeb3c2fd1814ce7489439f1d9367358140d54f388ae987fe75

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.662369Z digest=sha256:c58a114311d9395473c3c6496c43132b809c431ed1d576366e26419232d66def

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.725661Z digest=sha256:66ee0bdaff6feda25ca8193adc97601d5be603aac78331e2dfac552dbee971be

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.781368Z digest=sha256:2ae14316a3fac9ab41c4681a8a5daa947e61e763ddc31bbe7b11a68e74bf87a2

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:17.880052Z digest=sha256:a5bcec3111c5544b8fc56866277c7a5430cb57e8dde7c8594628ebe82d00108e

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=sha256:119c3a02255296d3f0ba7b0ed0e307f1f6e9aa81b452393bf0015bb25c076c58

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.023378Z digest=sha256:96b077baa08c9e3b85cd70fb25dde956855ff4efa9b79164e461c2e0805057af

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.078357Z digest=sha256:2382da10d89e9b214a20a834c6d6bed9c117cedb989901fca7ca6466b9eded6d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.153616Z digest=sha256:55d383473c21e3eb9e7f97386cf765b56e4131773b98da02e07d37878d4087cb

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.238359Z digest=sha256:8b1bda2dad77b19a30512a14b68253e8f01078fac2cdf283126352f144e41108

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.299350Z digest=sha256:303d9b6bb23228f3eac9bfd7d1d40323923db8df087b129fa2fb429f4a8dd086

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.334983Z digest=sha256:fcb4f59f79abb7c0755983cee64dbaff9869aa55a8e4332160a1e45a75f2867d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.410260Z digest=sha256:78b1bb398a50e9b54b11da312c3b1f4e6657662b88d2045dc0c02caa7731308f

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.492329Z digest=sha256:e01728c6d60ae850e14292d3abd77e0b6c52c88506ab438be7a123e053eeba1c

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.601416Z digest=sha256:11668e9499a9f7893da16c8fec78daac6885e1813c771ee1a461cfad4bcd101b

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.709075Z digest=sha256:01091802f8f2143dffadd5a71c71362cab09aded252107a93a9213b9fec58c1c

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.785536Z digest=sha256:0e01cb263caaeb9d5bc57689c355753fc2cf875770577c0e074ed1dcc61cb32d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.861785Z digest=sha256:197ea6737a5e871152b5b0d55c8f4d1abc84e5115849e580b3d5023e71e3be26

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:18.950980Z digest=sha256:07f6f13bf475c36cd2e897a09ff113bcc2e93936348f36ce76afd7601cc1f664

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.022885Z digest=sha256:063453b6fde40b33f4e1b989415c6f1ea1f4354ee37079678ac238fc3b6cc040

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.092651Z digest=sha256:9efccccc5a50e9725e6dd41bb724e67b69aaca0f33501b94dac2c121c6e75888

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.131180Z digest=sha256:93b3202f284de75ae5c2a0e696d0c5891457426044778f7cefe08cb99b330b6c

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.164735Z digest=sha256:1690d65e48f10dd1a8d172e72d434583f00ada2aec207a514d8da4e3b51109b3

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.228463Z digest=sha256:bb2e681ac76bb5f5c1008800eba014d76209c707e1cb269f75fcb44b4b814fb9

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.312064Z digest=sha256:39c798fe3b883780d1415ac6f990998f9ec27ca3247cf8391b4907088225c81a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.413664Z digest=sha256:14ccfc235fed2edd0fef588c4b14b8d8e8bc02f18dd0350bab135fc6ff70e597

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.444663Z digest=sha256:81c2b727ee7eee560af1bb869b77a58a8b32f9a2810f2840c7222cb4a963bba8

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.499058Z digest=sha256:571e082d2b0af010dc741b15644160b77dcdd41c02c777a47cf8e56a1eaf6b34

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T18:10:19.555298Z digest=sha256:c5274ac5a9143829e9020024cd722192f59faa095bcaf7fd1f6d82d02cace0c9

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-15T19:19:42.748573Z digest=sha256:ab229a2631b655a88316d70f2c3fc767e41376f9b0cb46e3f545f735e0d4a0f8

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=sha256:9348938cceadcb41cd0254612be87ead008b746890284fcd961de88fb8375a05

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:c0fc6ca47f87745bdfc23e4579449c91ce1c4237c34662467742d1f505394540

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-28T17:36:28.436212Z digest=sha256:85ed2a5cad4c26870a468aab3342ca27ce03532fafa825c6da9f9515d3dd4566

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-26T21:37:08.441371Z digest=sha256:1cc051ded64404aae3ce0f3993fed4958758852f69d5834ee20ed489400271a2

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-01T07:19:36.472441Z digest=sha256:1f0e6558f0d2213eb58362738ef70cba0957d0cffabbc60d8e7b3712f3c1c4e1