Paper Citation Record · LEDGER

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2411.17000.

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

pith.paper-citation-record.v1
2411.17000 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:41:01.390045Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T07:32:55.811041Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T07:32:59.960032Z

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4725338-430b-43a4-834d-b3367e76fec2 · outbound

This paper cites Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:02.140512Z

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Observation 477b5103-aa78-44e9-80d5-dc955230f180 · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.149734Z

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Observation 91ead0bf-a9c9-42c5-a576-e5a5bb8ab8d9 · outbound

This paper cites Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:02.115970Z

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Observation a23f5239-4d5a-4363-bd04-861185afa60d · outbound

This paper cites Preliminary Inter-Comparison between AHI, VIIRS and MODIS Clear-Sky Ocean Radiances for Accurate SST Retrievals.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Preliminary Inter-Comparison between AHI, VIIRS and MODIS Clear-Sky Ocean Radiances for Accurate SST Retrievals

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:02.093777Z

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Observation f3d20e0d-c933-4b5e-956f-2813fb5fbe7e · outbound

This paper cites Assessment of GOES-16/ABI middle wave infrared band using references of Himawari-8/AHI and Aqua/MODIS.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Assessment of GOES-16/ABI middle wave infrared band using references of Himawari-8/AHI and Aqua/MODIS

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:02.063344Z

Source-reported events for the cited work

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Observation e984e5fb-cb4a-4966-aead-330e58d65431 · outbound

This paper cites Deep residual learning for image recognition.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Deep residual learning for image recognition

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.175566Z

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Observation 4051ed1d-874e-4fcb-a756-22e33a9bb508 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Imagenet classification with deep convolutional neural networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.184438Z

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Observation 839976fe-816b-4d6b-aae8-840ecab7a06f · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 8

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Source-reported events for the cited work

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Observation d5f2a981-ec9b-4d2b-a9ef-fb4801a63c49 · outbound

This paper cites OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.199362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7684d48d-5e92-40e2-aaed-4bc035e9189d · outbound

This paper cites Fully convolutional networks for semantic segmentation.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Fully convolutional networks for semantic segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.204848Z

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Unavailable: canonical work link unavailable.

Observation 7cbfdc58-5c84-4944-aa94-fc4d029a7fcc · outbound

This paper cites Optimizing worldview-2,-3 cloud masking using machine learning approaches.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Optimizing worldview-2,-3 cloud masking using machine learning approaches

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.999859Z

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Observation eef63335-1fe3-466a-a61b-22dcfe676f61 · outbound

This paper cites Deep learning in remote sensing: A comprehensive review and list of resources.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Deep learning in remote sensing: A comprehensive review and list of resources

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.984471Z

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Observation 7673c24c-9ce6-4e24-9fc1-23e4277470e3 · outbound

This paper cites Attention is all you need.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Attention is all you need

Reference 13

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Observation 8c256cd7-2aec-4736-acd4-3fcda7db03e5 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.229403Z

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Observation 7650f84e-c0e3-44f3-b0a4-4ba29f4e6669 · outbound

This paper cites How Do Vision Transformers Work?.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery How Do Vision Transformers Work?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.233642Z

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Observation 538816b8-e623-494e-ba95-7c27472a5e4e · outbound

This paper cites Hls operational land imager surface reflectance and toa brightness daily global 30 m v2.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Hls operational land imager surface reflectance and toa brightness daily global 30 m v2

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.947080Z

Source-reported events for the cited work

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Observation 0206837d-56d7-474d-bfe4-3e9f3bb5aeaf · outbound

This paper cites MOD021KM MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km, 2017.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery MOD021KM MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km, 2017

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.928695Z

Source-reported events for the cited work

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Observation beb2b6c4-888d-498a-9d47-e4c5f58c443d · outbound

This paper cites Creating reprojected true color modis images: A tutorial.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Creating reprojected true color modis images: A tutorial

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.902938Z

Source-reported events for the cited work

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Observation 6dbef68d-c062-49c2-9a3c-0336f210f772 · outbound

This paper cites Creating raster omnimax images from multiple perspective views using the elliptical weighted average filter.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Creating raster omnimax images from multiple perspective views using the elliptical weighted average filter

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.885327Z

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Observation 278f3d15-d86e-408e-afc2-d5508cec971e · outbound

This paper cites MODIS level 1b product user’s guide, 2019.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery MODIS level 1b product user’s guide, 2019

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.870192Z

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Observation 1aa1c3ad-3554-4e5b-8940-e55ca86149f1 · outbound

This paper cites Satpy: A python library for weather satellite processing.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Satpy: A python library for weather satellite processing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.852700Z

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Observation cbc56c14-9351-4828-9cd9-5df3ac2e5a07 · outbound

This paper cites Batch normalization: accelerating deep network training by reducing internal covariate shift.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Batch normalization: accelerating deep network training by reducing internal covariate shift

Reference 22

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Observation f63c09ef-d2ff-4e71-99a3-b95211045255 · outbound

This paper cites Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.822492Z

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Observation b64752d0-e6d6-404e-bee0-5bcb914d2174 · outbound

This paper cites an unresolved cited work.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Unresolved cited work

Reference 24

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Observation 6c8cd6e8-0afb-495a-915f-7d2e32821dee · outbound

This paper cites Transformers in remote sensing: A survey.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Transformers in remote sensing: A survey

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.789442Z

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Observation 2b40c056-ae5e-4bae-bbfc-447eaf66e2d3 · outbound

This paper cites Simmim: A simple framework for masked image modeling.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Simmim: A simple framework for masked image modeling

Reference 26

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Source-reported events for the cited work

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Observation 989cec33-67f5-46e4-9b72-218cc6297d76 · outbound

This paper cites Massively multilingual neural machine translation.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Massively multilingual neural machine translation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.763529Z

Source-reported events for the cited work

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Observation 1e1ece90-76e7-43e8-ae34-976c300bb339 · outbound

This paper cites A Survey of the Self Supervised Learning Mechanisms for Vision Transformers.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery A Survey of the Self Supervised Learning Mechanisms for Vision Transformers

Reference 28

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Observation 0e068d4a-0339-4c5b-851c-e3ec7abf2b91 · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Swin transformer v2: Scaling up capacity and resolution

Reference 29

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Observation cc47de93-2046-4ac3-bcd0-e718d042350c · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Image quality assessment: from error visibility to structural similarity

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.315222Z

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Observation 1622c6b3-f51c-4af7-9b96-815dd18553f9 · outbound

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SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Unresolved cited work

Reference 31

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Observation b19ec2ff-7869-4f0e-92c7-54fea2b3092f · outbound

This paper cites Kirk Ayers.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Kirk Ayers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.709226Z

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Observation cc9ef25b-2977-4e15-a599-20d2ff35e42b · outbound

This paper cites A study of vertical cloud structure of the indian summer monsoon using cloudsat data.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery A study of vertical cloud structure of the indian summer monsoon using cloudsat data

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.694725Z

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Observation 639f041a-757a-4fa3-9202-8f2354267a56 · outbound

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SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Unresolved cited work

Reference 34

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Observation e9557255-3835-4412-8605-95f788abe657 · outbound

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SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Unresolved cited work

Reference 35

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Observation 87e3a182-84ef-4864-bf0c-c57a634e0c22 · outbound

This paper cites Meyer, Michael D.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Meyer, Michael D

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.644871Z

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Observation cdeff466-c817-4ce4-9e9b-73ed90ffbdee · outbound

This paper cites Haynes, Steven D.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Haynes, Steven D

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.629677Z

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Observation 558c4aa4-6465-45b4-a13c-eddb4404c09c · outbound

This paper cites Wu, and Leah Ding.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Wu, and Leah Ding

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.615347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation abee61bd-1eac-47d1-acfa-4b043e6ee73a · outbound

This paper cites Foley, Kirk D.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Foley, Kirk D

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.596319Z

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Observation 23b35617-b07b-4e78-b974-d6a46782530c · outbound

This paper cites Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:41:01.581732Z

Source-reported events for the cited work

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Observation 5b31c265-92c6-45df-9a06-b9e8adb0e716 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Pytorch: An imperative style, high-performance deep learning library

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.369550Z

Source-reported events for the cited work

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Observation 93e99af4-7047-4c58-ba30-309b48402e4b · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.375239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fc8b87a-e5e7-4391-a81a-a86d2551528c · outbound

This paper cites Decoupled weight decay regularization.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Decoupled weight decay regularization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.381755Z

Source-reported events for the cited work

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Observation 78c1d437-0e8d-463f-86cb-1a35fce00eeb · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery Super-convergence: Very fast training of neural networks using large learning rates

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.385899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e96b9398-8c4d-48aa-b263-8e4eb233053e · outbound

This paper cites A Study of BFLOAT16 for Deep Learning Training.

SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery A Study of BFLOAT16 for Deep Learning Training

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T12:41:01.390045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7e15980c-9919-4f7f-acd3-66d713f7d483 · inbound

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

TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:32:59.961329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.