Paper Citation Record · LEDGER

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation

As of 20 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2509.10919.

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

pith.paper-citation-record.v1
2509.10919 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:27:41.283002Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T04:37:52.241536Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

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unresolved20
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External citation measurements

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Outbound references

Observation 9f17e959-dd86-4e22-bcce-7d60187b34c9 · outbound

This paper cites Lobell, and Stefano Ermon.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Lobell, and Stefano Ermon

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:39.315118Z

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Unavailable: canonical work link unavailable.

Observation 6e197fd9-db33-48fe-9091-c301d5ae4cd6 · outbound

This paper cites an unresolved cited work.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:39.512188Z

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Observation 287e74b5-9abd-4494-ac52-03b82e93510c · outbound

This paper cites TerraMind: Large-scale generative multimodality for Earth Observation, 2025.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation TerraMind: Large-scale generative multimodality for Earth Observation, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:39.635717Z

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Observation c40a5c87-6869-4ae4-8c8e-61eaa4cdc9d6 · outbound

This paper cites Clay foundation model.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Clay foundation model

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:39.819661Z

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Observation 253d8b6b-bffc-487c-b5f6-2faddeea7adb · outbound

This paper cites Albrecht, and Xiao Xiang Zhu.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Albrecht, and Xiao Xiang Zhu

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.011528Z

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Observation df3dc81f-94ed-4931-8335-8b40e226edf3 · outbound

This paper cites Stewart, Nils Lehmann, Isaac A.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Stewart, Nils Lehmann, Isaac A

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.166281Z

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Observation 3c449f49-b240-4436-ad90-3616f8e1dd8d · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2022.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2022

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.247478Z

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Observation dfc40728-a05c-4b04-8f42-c2c82a2609a9 · outbound

This paper cites Scaling vision with sparse Mixture of Experts, 2021.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Scaling vision with sparse Mixture of Experts, 2021

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.332893Z

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Observation e88fd165-6acd-4a3d-9288-1c963a7d5e34 · outbound

This paper cites How lightweight can a Vision Transformer be, 2024.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation How lightweight can a Vision Transformer be, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.441044Z

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Observation 430d46d0-25b5-44a1-b099-fde88bb0cae7 · outbound

This paper cites Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.540382Z

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Observation 8b48ac75-447a-478a-be69-9aaa9f8d5450 · outbound

This paper cites Senpa-MAE: Sensor parameter aware masked autoencoder for multi-satellite self-supervised pretraining, 2024.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Senpa-MAE: Sensor parameter aware masked autoencoder for multi-satellite self-supervised pretraining, 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.580027Z

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Observation f524aaf1-a12e-4886-b34e-34a4e59642ea · outbound

This paper cites Masked autoencoders are scalable vision learners, 2021.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Masked autoencoders are scalable vision learners, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.648442Z

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Observation c02d139c-e9c9-4f4d-b092-b39d38c2ccaa · outbound

This paper cites GLU variants improve transformer, 2020.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation GLU variants improve transformer, 2020

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.720143Z

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Observation 8ba09598-51f7-4953-8b4c-f469c1af94b8 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated Mixture-of-Experts layer, 2017.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Outrageously large neural networks: The sparsely-gated Mixture-of-Experts layer, 2017

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.799532Z

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Observation 3f759028-0395-4bf7-a592-df0ab1777f39 · outbound

This paper cites Landsat-Bench: Datasets and benchmarks for Landsat foundation models, 2025.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Landsat-Bench: Datasets and benchmarks for Landsat foundation models, 2025

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.868553Z

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Observation f61ff8af-49ff-45af-90fa-a4923799115f · outbound

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

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation BigEarthNet: A large-scale benchmark archive for remote sensing image understanding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:40.926745Z

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Observation 372dff28-05b4-4c53-a000-1ce29fd36ffa · outbound

This paper cites EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification, 2019.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification, 2019

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:41.010839Z

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Observation 07ea1e59-8ffc-4726-b9c9-479b9ed0c37f · outbound

This paper cites Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:41.087589Z

Source-reported events for the cited work

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Observation ccb603da-b0bf-49e0-b9d7-1c793a384a50 · outbound

This paper cites SatlasPretrain: A large-scale dataset for remote sensing image understanding, 2023.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation SatlasPretrain: A large-scale dataset for remote sensing image understanding, 2023

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:41.222216Z

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Observation f48bd4c2-b2d0-4ff3-a00e-2bde81e6d852 · outbound

This paper cites Prithvi-EO-2.0: A versatile multi-temporal foundation model for Earth Observation applications.

Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation Prithvi-EO-2.0: A versatile multi-temporal foundation model for Earth Observation applications

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T17:27:41.283002Z

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Pith citing papers

Observation 42c2dd0c-c448-4112-95e1-7bce21180a80 · inbound

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring cites this paper.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T04:37:52.241536Z

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Unavailable: canonical work link unavailable.