Paper Citation Record · LEDGER

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2505.14951.

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

pith.paper-citation-record.v1
2505.14951 v1

Coverage vector

measured 34 of 34 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:08:13.297661Z

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

Source: pith, observed 2026-08-06T17:08:16.157991Z

Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 8abeaa84-cf6c-43ad-a98e-ef34afc4c09e · outbound

This paper cites Multimae: Multi-modal multi-task masked autoen- coders.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Multimae: Multi-modal multi-task masked autoen- coders

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:58.407649Z

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Observation 103c181d-ca92-4271-8cde-eb9fe46fa57a · outbound

This paper cites Satlaspretrain: A large- scale dataset for remote sensing image understanding.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Satlaspretrain: A large- scale dataset for remote sensing image understanding

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:58.180477Z

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Observation c043bd61-342e-4b5a-ba6b-0810ce526277 · outbound

This paper cites HLS Multi Temporal Crop Classification, 2023.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks HLS Multi Temporal Crop Classification, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.934653Z

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Observation bd5c0250-b76b-41cf-a2a9-afe910db9356 · outbound

This paper cites Satmae: Pre-training transformers for tem- poral and multi-spectral satellite imagery.NeurIPS, 35:197– 211, 2022.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Satmae: Pre-training transformers for tem- poral and multi-spectral satellite imagery.NeurIPS, 35:197– 211, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.830678Z

Source-reported events for the cited work

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Observation 9b296322-03bd-43ea-9227-6a46225e8db9 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Imagenet: A large-scale hierarchical image database

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:51.806284Z

Source-reported events for the cited work

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Observation 25c6e75f-a217-47e4-9a8c-886635a3df7e · outbound

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

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:51.936537Z

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Observation c654ea9e-0232-4f7c-9d08-ac6cba409b5a · outbound

This paper cites Croma: Remote sensing representations with contrastive radar- optical masked autoencoders.NeurIPS, 36, 2024.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Croma: Remote sensing representations with contrastive radar- optical masked autoencoders.NeurIPS, 36, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.696017Z

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Observation 30ef9799-0805-4057-a7a9-a770b8ee3c4c · outbound

This paper cites Skysense: A multi-modal remote sens- ing foundation model towards universal interpretation for earth observation imagery.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Skysense: A multi-modal remote sens- ing foundation model towards universal interpretation for earth observation imagery

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.586494Z

Source-reported events for the cited work

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Observation b3ff44e5-cb70-4706-9018-9270e62cfc1c · outbound

This paper cites Masked autoencoders are scalable vision learners.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Masked autoencoders are scalable vision learners

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.495808Z

Source-reported events for the cited work

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Observation ece778ca-2009-45ec-b9bd-77e3778c8ff3 · outbound

This paper cites an unresolved cited work.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Unresolved cited work

Reference 10

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Observation 304e5bbc-e06a-4e57-a0d0-9a73b31ece0c · outbound

This paper cites Masked Image Modeling: A Survey.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Masked Image Modeling: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:52.563283Z

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Observation 80fee846-169f-4578-8233-add4a506f278 · outbound

This paper cites Masked autoencoders that listen.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Masked autoencoders that listen

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.218245Z

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Observation fc78dbde-2c4c-4696-a112-9268c208b36a · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:52.735664Z

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Observation f32c05a6-7d51-483e-9d03-e173e48032d4 · outbound

This paper cites Geo- bench: Toward foundation models for earth monitoring.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Geo- bench: Toward foundation models for earth monitoring

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:57.077347Z

Source-reported events for the cited work

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Observation 8630e3c1-09c5-41f1-ba7a-9154ef8524be · outbound

This paper cites Multimodality helps unimodality: Cross- modal few-shot learning with multimodal models.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Multimodality helps unimodality: Cross- modal few-shot learning with multimodal models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:56.960366Z

Source-reported events for the cited work

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Observation b2ea4498-0e3e-4906-af59-5968c43aef6a · outbound

This paper cites Re- moteclip: A vision language foundation model for remote sensing.IEEE Transactions on Geoscience and Remote Sensing, 2024.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Re- moteclip: A vision language foundation model for remote sensing.IEEE Transactions on Geoscience and Remote Sensing, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:56.854441Z

Source-reported events for the cited work

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Observation bb8b4295-8a4b-41c9-aa36-d00daf446721 · outbound

This paper cites A convnet for the 2020s.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks A convnet for the 2020s

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:56.716250Z

Source-reported events for the cited work

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Observation c22a0db8-849d-439d-aecf-b0511898dd12 · outbound

This paper cites MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:53.100039Z

Source-reported events for the cited work

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Observation 0b75565f-c42a-4228-a242-2406af6445e1 · outbound

This paper cites Rethinking transformers pre-training for multi- spectral satellite imagery.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Rethinking transformers pre-training for multi- spectral satellite imagery

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:56.488338Z

Source-reported events for the cited work

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Observation e462cdb5-2da5-49d6-8a8c-83fdd31691ce · outbound

This paper cites How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:53.273043Z

Source-reported events for the cited work

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Observation 10cc8de0-b885-4996-908d-29d742858b71 · outbound

This paper cites Ssl4eo-l: Datasets and foundation models for landsat imagery.Advances in Neural Information Processing Systems, 36:59787–59807,.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Ssl4eo-l: Datasets and foundation models for landsat imagery.Advances in Neural Information Processing Systems, 36:59787–59807,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:56.258370Z

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Observation c21eeaab-db24-4163-bf4a-aab4f24a837e · outbound

This paper cites Torch- geo: deep learning with geospatial data.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Torch- geo: deep learning with geospatial data

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:56.024179Z

Source-reported events for the cited work

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Observation 9ae240aa-34bf-4b6a-9d9d-a9fb5259c9f6 · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:55.826704Z

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Observation 3d7d1e59-244a-4fbc-9c51-2f5aef01a770 · outbound

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

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Simmim: A simple framework for masked image modeling

Reference 24

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Observation 0222f482-3379-4dda-a424-a4dd1b8dc624 · outbound

This paper cites EarthNets: Empowering AI in Earth Observation.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks EarthNets: Empowering AI in Earth Observation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:53.796800Z

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Observation b9a84169-9ba2-4401-bc9c-325d5c3bb5e0 · outbound

This paper cites Neural plasticity-inspired foundation model for observing the earth crossing modalities.arXiv e-prints, pages arXiv–2403, 2024.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Neural plasticity-inspired foundation model for observing the earth crossing modalities.arXiv e-prints, pages arXiv–2403, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:55.694192Z

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Observation d41d6eb0-0403-463d-8662-0eba0f109097 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Depth anything: Unleashing the power of large-scale unlabeled data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:55.589747Z

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Observation 95d7c543-69a8-48b5-8026-4be353fd28b1 · outbound

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MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:55.493268Z

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Observation f6b4b861-4512-4648-8e56-2911dac23509 · outbound

This paper cites an unresolved cited work.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:55.391866Z

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Observation ab13c586-0fcc-453f-9e67-00bb951be622 · outbound

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MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:55.238206Z

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Observation 53335596-d842-4a59-8443-1322123d38b7 · outbound

This paper cites Overall, [14] comprises multiple modified versions of standard geospatial datasets for classification and segmen- tation tasks.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Overall, [14] comprises multiple modified versions of standard geospatial datasets for classification and segmen- tation tasks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:55.031397Z

Source-reported events for the cited work

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Observation 79d90d38-e04f-4499-af64-20188460bc06 · outbound

This paper cites Pre-training objective We pre-train our approach (depicted in Figure 2) using six input modalities: RGB, IRED, SIRED, EB, DEPTH, and SEG.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Pre-training objective We pre-train our approach (depicted in Figure 2) using six input modalities: RGB, IRED, SIRED, EB, DEPTH, and SEG

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:54.918948Z

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Observation d187ac60-e375-4b51-93e7-a4a2f9d796aa · outbound

This paper cites Fine-tuning setups for segmentation and classification EO tasks.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Fine-tuning setups for segmentation and classification EO tasks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:54.788137Z

Source-reported events for the cited work

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Observation 744ff84f-45e1-41f5-adf1-1287d2c7d4c4 · outbound

This paper cites Pre-training visualisations Masked input Prediction Masked input Prediction TargetTarget RGBDEPTHSEGRGBDEPTHSEG Figure 5.

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks Pre-training visualisations Masked input Prediction Masked input Prediction TargetTarget RGBDEPTHSEGRGBDEPTHSEG Figure 5

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:54.690361Z

Source-reported events for the cited work

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Pith citing papers

Observation d834033a-ee40-40b4-b0da-f4d01a61f1b0 · inbound

Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery cites this paper.

Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:08:16.257595Z

Source-reported events for the cited work

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