Paper Citation Record · LEDGER

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 11 inbound Pith citation observations for arXiv:2502.09356.

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

pith.paper-citation-record.v1
2502.09356 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:51:15.955147Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:01:54.270240Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T19:07:35.337379Z

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy9
unresolved15
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc0947e3-38ee-4961-88f3-e2882d856ea5 · outbound

This paper cites T., Dobrowski, S.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities T., Dobrowski, S

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.869439Z

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Observation 658746ba-2967-4c8f-a3d7-94ca361b5dee · outbound

This paper cites Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact (Version. 2.0).

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact (Version. 2.0)

Reference 4

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Observation 20bf64ba-8852-49cc-9480-7570aa5f1026 · outbound

This paper cites ASU researcher combats food insecurity with AI.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities ASU researcher combats food insecurity with AI

Reference 11

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Observation 8d7901e8-c944-4f99-a4de-a0e55c2978ec · outbound

This paper cites Kruse, C., Boyda, E., Chen, S., Karra, K., Bou-Nahra, T., Hammer, D., Mathis, J., Maddalene, T., Jambeck, J., and Laurier, F.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Kruse, C., Boyda, E., Chen, S., Karra, K., Bou-Nahra, T., Hammer, D., Mathis, J., Maddalene, T., Jambeck, J., and Laurier, F

Reference 12

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Observation eb7b79c3-4c79-4bea-a411-b7830f4898f4 · outbound

This paper cites an unresolved cited work.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Unresolved cited work

Reference 13

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Observation 6269c7e5-ce5a-44a5-a8ee-1a065ef3786c · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Representation Learning with Contrastive Predictive Coding

Reference 14

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Observation 6c7468a6-ab4f-4d71-ac62-cdcb5e75be19 · outbound

This paper cites Bigearthnet: A large-scale benchmark archive for remote sensing image understanding.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Bigearthnet: A large-scale benchmark archive for remote sensing image understanding

Reference 15

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Observation 3b432f88-b4db-4227-a408-50a9f427c4f5 · outbound

This paper cites E., Blu- menstiel, B., Ghosal, R., de Oliveira, P.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities E., Blu- menstiel, B., Ghosal, R., de Oliveira, P

Reference 16

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Observation 533f9222-805f-4fe3-a007-0f11b95ba492 · outbound

This paper cites Lightweight, Pre-trained Transformers for Remote Sensing Timeseries.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 17

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Source-reported events for the cited work

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Observation 4e539e94-1694-4adf-a8e6-c98bc9fc2bd6 · outbound

This paper cites Decoupling Common and Unique Representations for Multimodal Self-supervised Learning.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Decoupling Common and Unique Representations for Multimodal Self-supervised Learning

Reference 20

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Observation cbd6b6c4-d04c-4d3a-a4aa-0f2f42419a71 · outbound

This paper cites Esa worldcover 10 m 2021 v200.ESA WorldCover Project,.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Esa worldcover 10 m 2021 v200.ESA WorldCover Project,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.827714Z

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Observation a96e146d-11a0-468e-b540-0323623a46cf · outbound

This paper cites online” encoder computes patch encodings z1 =E(x 1), while our “target.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities online” encoder computes patch encodings z1 =E(x 1), while our “target

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.818320Z

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Observation 3d9e3a51-1dc0-412d-a5de-985b58629287 · outbound

This paper cites For each tile, we compute two feature sets: 1 the number of pixels within each WorldCover classification class, and 2 the latitude and longitude of the tile.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities For each tile, we compute two feature sets: 1 the number of pixels within each WorldCover classification class, and 2 the latitude and longitude of the tile

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.807631Z

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Observation d5092963-97de-4ebd-b3ed-4d1abebc465d · outbound

This paper cites MADOS and Sen1Floods11 (% mIoU) via linear probing.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities MADOS and Sen1Floods11 (% mIoU) via linear probing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.797020Z

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Observation e57897d4-1c5c-4042-897d-53d83a0be935 · outbound

This paper cites GPU-hours describes the number of GPU-hours required to pretrain each model for500epochs on an H100 GPU.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities GPU-hours describes the number of GPU-hours required to pretrain each model for500epochs on an H100 GPU

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.786627Z

Source-reported events for the cited work

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Observation 8c372d40-34a6-4bbe-8496-712d12ae0a58 · outbound

This paper cites In addition, we use the1%,5%and20%partitions shared by GeoBench.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities In addition, we use the1%,5%and20%partitions shared by GeoBench

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:51:16.775543Z

Source-reported events for the cited work

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Observation a89e029c-6606-4426-a05b-8b9cc4a952e2 · outbound

This paper cites Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward

Reference 1979

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Observation 5f1910fc-d66d-442a-b147-acc60af582ad · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2009

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Observation e8d3198a-4c47-47a3-af03-8b57e9e7ce70 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Efficient Estimation of Word Representations in Vector Space

Reference 2013

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Observation 91601258-5815-4711-94c0-2bff4802e973 · outbound

This paper cites The Kinetics Human Action Video Dataset.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities The Kinetics Human Action Video Dataset

Reference 2016

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Source-reported events for the cited work

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Observation ee8515e5-2713-4aaf-a25e-5ba8134fdb07 · outbound

This paper cites Introducing ai2’s beaker.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Introducing ai2’s beaker

Reference 2017

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Source-reported events for the cited work

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Observation bc433c44-11ab-4103-9380-b670b84c3603 · outbound

This paper cites Van Tricht, K., Degerickx, J., Gilliams, S., Zanaga, D., Bat- tude, M., Grosu, A., Brombacher, J., Lesiv, M., Bayas, J.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Van Tricht, K., Degerickx, J., Gilliams, S., Zanaga, D., Bat- tude, M., Grosu, A., Brombacher, J., Lesiv, M., Bayas, J

Reference 2018

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Observation 319f8ac0-0cd4-4e9f-a0f2-d6ea3a3f1f6d · outbound

This paper cites The era5 global reanalysis.Quarterly Journal of the Royal Meteorological Society, 146(730): 1999–2049,.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities The era5 global reanalysis.Quarterly Journal of the Royal Meteorological Society, 146(730): 1999–2049,

Reference 2019

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Observation cbb5f584-bd21-4a05-9acb-396d15d8a662 · outbound

This paper cites Augment your batch: better training with larger batches.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Augment your batch: better training with larger batches

Reference 2020

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Observation 50b6e713-4e3c-4b7d-976e-3493833bb0b2 · outbound

This paper cites Learning and Leveraging World Models in Visual Representation Learning.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Learning and Leveraging World Models in Visual Representation Learning

Reference 2021

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Observation 7e7fcce7-b010-480f-a3ae-feac08800562 · outbound

This paper cites J., Hanna, J., Borth, D., Papoutsis, I., Saux, B.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities J., Hanna, J., Borth, D., Papoutsis, I., Saux, B

Reference 2022

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Observation 0f1d4d0b-b9d4-49d3-9350-5da45570318c · outbound

This paper cites AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities

Reference 2023

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Observation aae2d7ae-e2da-471e-9511-a25de00e2df0 · outbound

This paper cites Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

Reference 2024

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Pith citing papers

Observation 0c0dd3ea-ee4c-442c-add7-95abc224caf6 · inbound

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation cites this paper.

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 28

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Position Prediction Self-Supervised Learning for Multimodal Satellite Imagery Semantic Segmentation cites this paper.

Position Prediction Self-Supervised Learning for Multimodal Satellite Imagery Semantic Segmentation Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 20

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Observation 3b75d08b-8493-401c-bdfe-aafd082d153e · inbound

Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars cites this paper.

Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 12

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Observation dc5c51da-6bef-441f-aa05-bfe7051fac6f · inbound

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data cites this paper.

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 24

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Observation 89982a21-c38e-4e20-8ef9-b0c6b8a6bfc9 · 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 Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 20

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Observation ec2f8cdb-9ea4-4b47-95ab-1000e0cde293 · inbound

Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation cites this paper.

Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 7

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Observation 825efc5e-f255-445d-bfc0-398fefb57d8f · inbound

Cryo-Bench: Benchmarking Foundation Models for Cryosphere Applications cites this paper.

Cryo-Bench: Benchmarking Foundation Models for Cryosphere Applications Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 27

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Observation a9c28b7b-9a4a-418a-99b4-15ba549824c3 · inbound

The Lov\'{a}sz Local Lemma: Foundations and Applications cites this paper.

The Lov\'{a}sz Local Lemma: Foundations and Applications Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 13

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Observation d90424d0-5c1a-43fa-a30f-f710a875eab6 · inbound

MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications cites this paper.

MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 72

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Observation b6734d58-63c8-40dc-bffa-2886200b8cf5 · inbound

TESSERA v2: Scaling Pixel-wise Earth Foundation Models cites this paper.

TESSERA v2: Scaling Pixel-wise Earth Foundation Models Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 24

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Observation 3ba439c8-4362-43af-9cd0-3a95d984e0dc · inbound

Scalable and Trustworthy Earth Observation Foundation Models cites this paper.

Scalable and Trustworthy Earth Observation Foundation Models Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.