No One Knows the State of the Art in Geospatial Foundation Models | Paper Citation Record

Observation a0edf6d3-3b7a-4580-a061-c1f286e6db7f · outbound

This paper cites Omnisat: Self- supervised modality fusion for earth observation.

No One Knows the State of the Art in Geospatial Foundation Models Omnisat: Self- supervised modality fusion for earth observation

Reference 1

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Observation d060de42-69e7-4aa5-9ce9-2af41d405a7b · outbound

This paper cites Anysat: One earth observation model for many resolutions, scales, and modalities.

No One Knows the State of the Art in Geospatial Foundation Models Anysat: One earth observation model for many resolutions, scales, and modalities

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.870223Z

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Observation cb40aa3d-6afe-42f3-b166-7ae89f808182 · outbound

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

No One Knows the State of the Art in Geospatial Foundation Models Satlaspretrain: A large-scale dataset for remote sensing image understanding

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.862522Z

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Observation 9b4c4b56-fb2c-4135-96bc-ca91e226e20b · outbound

This paper cites Olmoearth: Stable latent image modeling for multimodal earth observation.

No One Knows the State of the Art in Geospatial Foundation Models Olmoearth: Stable latent image modeling for multimodal earth observation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.867103Z

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Observation 44247a12-586a-4020-9694-770273705155 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

No One Knows the State of the Art in Geospatial Foundation Models On the Opportunities and Risks of Foundation Models

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T21:19:28.509360Z

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Observation f0c21e57-14df-439f-9abf-6d9a0d33049d · outbound

This paper cites Louis, G.

No One Knows the State of the Art in Geospatial Foundation Models Louis, G

Reference 6

Resolution: malformed identifier; doi_truncated, observed 2026-05-14T21:07:58.939577Z

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Observation 60fd4dae-557f-4fcf-a017-87a0ba5bf241 · outbound

This paper cites Unreproducible research is reproducible.

No One Knows the State of the Art in Geospatial Foundation Models Unreproducible research is reproducible

Reference 7

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Observation 2bb40915-4d77-41f2-991b-8644d307602b · outbound

This paper cites Accounting for variance in machine learning benchmarks.

No One Knows the State of the Art in Geospatial Foundation Models Accounting for variance in machine learning benchmarks

Reference 8

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Observation 0049b0d9-8eca-432e-ad9b-699b51a61db4 · outbound

This paper cites AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data.

No One Knows the State of the Art in Geospatial Foundation Models AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:13:43.902893Z

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Observation 4fc8c856-a8e3-4fc4-a3f6-e0950de06229 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.

No One Knows the State of the Art in Geospatial Foundation Models Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901

Reference 10

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Observation 40eea8b5-7d67-4923-9b6d-0fd8733a0f7a · outbound

This paper cites Emerging properties in self-supervised vision transformers.

No One Knows the State of the Art in Geospatial Foundation Models Emerging properties in self-supervised vision transformers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.730868Z

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Observation e626e6f8-f93c-4e68-bf10-16efa38ff995 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

No One Knows the State of the Art in Geospatial Foundation Models Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.847126Z

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Observation eaad0c00-fed7-4b44-b4fd-9989dfb3048e · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.Proceedings of the IEEE, 105(10):1865–1883.

No One Knows the State of the Art in Geospatial Foundation Models Remote sensing image scene classification: Benchmark and state of the art.Proceedings of the IEEE, 105(10):1865–1883

Reference 13

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Observation 5a6b48c4-6650-4e27-849e-afff6bac38e0 · outbound

This paper cites Functional map of the world.

No One Knows the State of the Art in Geospatial Foundation Models Functional map of the world

Reference 14

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Observation 71337712-7af4-4a90-90fa-629681ce2912 · outbound

This paper cites In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Work- shops (CVPR W), pp.

No One Knows the State of the Art in Geospatial Foundation Models In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Work- shops (CVPR W), pp

Reference 15

Resolution: malformed identifier; arxiv_id, observed 2026-05-14T21:19:28.518439Z

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Observation 0f7f6242-c470-46f8-86e1-1617face2287 · outbound

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

No One Knows the State of the Art in Geospatial Foundation Models TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.505309Z

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Observation af9c5416-b4e6-4061-ba84-e120816aca31 · outbound

This paper cites The Benchmark Lottery.

No One Knows the State of the Art in Geospatial Foundation Models The Benchmark Lottery

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.538296Z

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Observation 382ff242-2b64-4847-a01e-54ed1736193f · outbound

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

No One Knows the State of the Art in Geospatial Foundation Models Imagenet: A large- scale hierarchical image database

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.765017Z

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Observation 76e831cc-8441-454a-8d28-5daedf40d00d · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

No One Knows the State of the Art in Geospatial Foundation Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.767618Z

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Observation 2a30831f-11bd-4a99-8857-5626637b5e54 · outbound

This paper cites Data science at the singularity.Harvard Data Science Review, 6(1).

No One Knows the State of the Art in Geospatial Foundation Models Data science at the singularity.Harvard Data Science Review, 6(1)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.829996Z

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Observation 57a56fdd-0239-4c91-94e8-54bbfae81e84 · outbound

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

No One Knows the State of the Art in Geospatial Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 21

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Observation 0898ed4d-7164-47f2-bee5-98d31e4c9373 · outbound

This paper cites Phileo bench: Evaluating geo-spatial foundation models.

No One Knows the State of the Art in Geospatial Foundation Models Phileo bench: Evaluating geo-spatial foundation models

Reference 22

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Observation 40517f73-0a5d-40e6-85a4-ef17a3bdb6c3 · outbound

This paper cites Open LLM leaderboard v2.

No One Knows the State of the Art in Geospatial Foundation Models Open LLM leaderboard v2

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.810419Z

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Observation a58486f5-0eef-4a0a-a447-e722d54025b9 · outbound

This paper cites Major tom: Expandable datasets for earth observation.

No One Knows the State of the Art in Geospatial Foundation Models Major tom: Expandable datasets for earth observation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.813067Z

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Observation d67023b5-20fc-4c12-a874-b1182cebf471 · outbound

This paper cites Bad tables: Why you shouldn’t trust results tables in remote-sensing founda- tion model papers.

No One Knows the State of the Art in Geospatial Foundation Models Bad tables: Why you shouldn’t trust results tables in remote-sensing founda- tion model papers

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.864905Z

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Observation 0dadcf74-fd4f-4dbf-ab7b-ff93896d355a · outbound

This paper cites Croma: Remote sensing representations with contrastive radar-optical masked autoencoders.Advances in Neural Information Processing Systems, 36:5506–5538.

No One Knows the State of the Art in Geospatial Foundation Models Croma: Remote sensing representations with contrastive radar-optical masked autoencoders.Advances in Neural Information Processing Systems, 36:5506–5538

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.807846Z

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Observation 9851bbc2-8023-43ea-838f-0f25d9e0d354 · outbound

This paper cites A framework for few-shot language model evaluation.Zenodo, 2024.lm-evaluation-harness.

No One Knows the State of the Art in Geospatial Foundation Models A framework for few-shot language model evaluation.Zenodo, 2024.lm-evaluation-harness

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.775284Z

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Observation 0863d5ed-774e-4fb0-b584-9e1b0c4bf103 · outbound

This paper cites Flair: a country-scale land cover semantic segmentation dataset from multi-source optical imagery.Advances in Neural Information Processing Systems, 36:16456–16482.

No One Knows the State of the Art in Geospatial Foundation Models Flair: a country-scale land cover semantic segmentation dataset from multi-source optical imagery.Advances in Neural Information Processing Systems, 36:16456–16482

Reference 28

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Observation 7faec46e-51f2-4fb7-bcf7-3a4bff8cf022 · outbound

This paper cites Terratorch: The geospatial foundation models toolkit.

No One Knows the State of the Art in Geospatial Foundation Models Terratorch: The geospatial foundation models toolkit

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.801679Z

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Observation 555829fd-8698-4471-9ce2-44029ffcdd74 · outbound

This paper cites Deep residual learning for image recognition.

No One Knows the State of the Art in Geospatial Foundation Models Deep residual learning for image recognition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.804701Z

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Observation 94cd179c-76d4-4917-99a1-9a75a70049cd · outbound

This paper cites an unresolved cited work.

No One Knows the State of the Art in Geospatial Foundation Models Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-05-15T12:40:36.839574Z

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Observation ce5f74fb-caef-4306-9f86-bc178592cf6c · outbound

This paper cites RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning.

No One Knows the State of the Art in Geospatial Foundation Models RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-08-18T03:17:13.055583Z

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Observation 1efcd4bc-ffe3-4099-ab56-21d4e8076a19 · outbound

This paper cites Mdas: A new multimodal benchmark dataset for remote sensing.Earth System Science Data, 15(1):113–131.

No One Knows the State of the Art in Geospatial Foundation Models Mdas: A new multimodal benchmark dataset for remote sensing.Earth System Science Data, 15(1):113–131

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.716337Z

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Observation 97dee1d0-5015-436a-9d54-bbcb380403d7 · outbound

This paper cites Generic knowledge boosted pretraining for remote sensing images.IEEE Transactions on Geoscience and Remote Sensing, 62:1–13.

No One Knows the State of the Art in Geospatial Foundation Models Generic knowledge boosted pretraining for remote sensing images.IEEE Transactions on Geoscience and Remote Sensing, 62:1–13

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.718707Z

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Observation 2d1f5a7a-b0dc-4340-8d86-f13dcda92ea0 · outbound

This paper cites an unresolved cited work.

No One Knows the State of the Art in Geospatial Foundation Models Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-05-15T12:40:36.797821Z

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Observation 5ec92147-ad9d-4d78-8e67-4373839b2333 · outbound

This paper cites Spatial depen- dence between training and test sets: another pitfall of classification accuracy assessment in remote sensing.Machine Learning, 111:2715–2740.

No One Knows the State of the Art in Geospatial Foundation Models Spatial depen- dence between training and test sets: another pitfall of classification accuracy assessment in remote sensing.Machine Learning, 111:2715–2740

Reference 36

Resolution: verified exact; doi, observed 2026-05-14T21:07:58.935773Z

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Observation d481643a-361a-42b8-aabc-4378c5e50081 · outbound

This paper cites Mahecha, and Carsten F.

No One Knows the State of the Art in Geospatial Foundation Models Mahecha, and Carsten F

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:07:58.932512Z

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Observation c1983ebc-f87e-407c-aefb-36fefc7a5881 · outbound

This paper cites Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance.

No One Knows the State of the Art in Geospatial Foundation Models Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:19:28.544554Z

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Observation a7093ad0-c256-43ba-8adf-078e14a31fb8 · outbound

This paper cites Segment anything.

No One Knows the State of the Art in Geospatial Foundation Models Segment anything

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.816021Z

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Observation 06e242ef-7355-4a78-8dd1-b5974c4eb272 · outbound

This paper cites an unresolved cited work.

No One Knows the State of the Art in Geospatial Foundation Models Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-05-15T12:40:36.744280Z

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Observation 96680da7-3927-4fb3-a606-06e840876870 · outbound

This paper cites GEO-Bench: Toward foundation models for earth monitoring.

No One Knows the State of the Art in Geospatial Foundation Models GEO-Bench: Toward foundation models for earth monitoring

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.711082Z

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Observation dfc040db-e8dc-400c-894e-0f5234573a3f · outbound

This paper cites Geo-bench: Toward foundation models for earth monitoring.Advances in Neural Information Processing Systems, 36:51080–51093.

No One Knows the State of the Art in Geospatial Foundation Models Geo-bench: Toward foundation models for earth monitoring.Advances in Neural Information Processing Systems, 36:51080–51093

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.769992Z

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Observation ed19b267-0f49-4b0a-b489-fad1744b453e · outbound

This paper cites Object detection in optical remote sensing images: A survey and a new benchmark.ISPRS journal of photogrammetry and remote sensing, 159:296–307.

No One Knows the State of the Art in Geospatial Foundation Models Object detection in optical remote sensing images: A survey and a new benchmark.ISPRS journal of photogrammetry and remote sensing, 159:296–307

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.849871Z

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Observation 23d31581-85e4-4215-aef6-c57e569afd8f · outbound

This paper cites Masked angle-aware autoencoder for remote sensing images.

No One Knows the State of the Art in Geospatial Foundation Models Masked angle-aware autoencoder for remote sensing images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.753054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b303df0c-a8c6-4705-846e-aebbb4936d03 · outbound

This paper cites Holistic evaluation of language models.

No One Knows the State of the Art in Geospatial Foundation Models Holistic evaluation of language models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.790256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae77a52c-9e60-431b-8432-a4a2cfa7a612 · outbound

This paper cites Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research.

No One Knows the State of the Art in Geospatial Foundation Models Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.762461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2a7b40cf-15a7-4737-a2d3-674798c35f95 · outbound

This paper cites Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion.

No One Knows the State of the Art in Geospatial Foundation Models Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.523157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b8fad2e1-b6c5-47d3-9871-bf4fc31a0dcc · outbound

This paper cites an unresolved cited work.

No One Knows the State of the Art in Geospatial Foundation Models Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-15T12:40:36.780097Z

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Observation 66b4b306-2ede-44ef-9bfe-8c69df12878a · outbound

This paper cites Zimmer-Dauphinee, et al.

No One Knows the State of the Art in Geospatial Foundation Models Zimmer-Dauphinee, et al

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.737546Z

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Observation 4a8996d7-a21f-4c7f-bff6-e4d3c96d65cf · outbound

This paper cites Sea- sonal contrast: Unsupervised pre-training from uncurated remote sensing data.

No One Knows the State of the Art in Geospatial Foundation Models Sea- sonal contrast: Unsupervised pre-training from uncurated remote sensing data

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.728449Z

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Observation 99530bea-e428-4990-94fe-2b0281aea3b0 · outbound

This paper cites PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models.

No One Knows the State of the Art in Geospatial Foundation Models PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models

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Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.534901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1607ebf2-dbee-4e1f-9cc5-574b9ba635e9 · outbound

This paper cites Towards geospatial foundation models via continual pretraining.

No One Knows the State of the Art in Geospatial Foundation Models Towards geospatial foundation models via continual pretraining

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.746730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 32b50ae9-29f7-4383-bb11-25708e28dd49 · outbound

This paper cites Mmearth: Exploring multi-modal pretext tasks for geospatial representation learning.

No One Knows the State of the Art in Geospatial Foundation Models Mmearth: Exploring multi-modal pretext tasks for geospatial representation learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.784678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e542af89-c7ba-4ee8-885f-65c451e1ab80 · outbound

This paper cites Mapping global dynamics of benchmark creation and saturation in artificial intelligence.Nature Communications.

No One Knows the State of the Art in Geospatial Foundation Models Mapping global dynamics of benchmark creation and saturation in artificial intelligence.Nature Communications

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.852287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e96143e1-6777-487b-93a4-b5c8e7eecd78 · outbound

This paper cites Planted: a dataset for planted forest identification from multi- satellite time series.

No One Knows the State of the Art in Geospatial Foundation Models Planted: a dataset for planted forest identification from multi- satellite time series

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.787099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 42d204cb-0082-4151-8ccc-212308eef071 · outbound

This paper cites Learning transferable visual models from natural language supervision.

No One Knows the State of the Art in Geospatial Foundation Models Learning transferable visual models from natural language supervision

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.823016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 83f049e5-dd9d-44a0-bf2b-003805646ced · outbound

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

No One Knows the State of the Art in Geospatial Foundation Models Scale-mae: A scale- aware masked autoencoder for multiscale geospatial representation learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.819057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 98a63e45-7ff2-4e2b-87ff-776b12403c1b · outbound

This paper cites Position: Mission critical – satellite data is a distinct modality in machine learning.ICML.

No One Knows the State of the Art in Geospatial Foundation Models Position: Mission critical – satellite data is a distinct modality in machine learning.ICML

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.782518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e38e218a-b1c4-41e7-a3c7-5427a675799a · outbound

This paper cites SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion.

No One Knows the State of the Art in Geospatial Foundation Models SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:19:28.541589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ab75596-f5cf-49d6-8584-8fa790e6af26 · outbound

This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294.

No One Knows the State of the Art in Geospatial Foundation Models Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.750920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db6fe9a1-c8b2-402b-828d-736831fd1dd4 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

No One Knows the State of the Art in Geospatial Foundation Models Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.755192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d515f8dc-f6c6-4b87-87fc-fa472b735754 · outbound

This paper cites Geo- bench-2: From performance to capability, rethinking evaluation in geospatial ai.

No One Knows the State of the Art in Geospatial Foundation Models Geo- bench-2: From performance to capability, rethinking evaluation in geospatial ai

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Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.555252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ac2b690-ff59-45ee-a2fc-abc517faaabe · outbound

This paper cites Earthdial: Turning multi-sensory earth observations to interactive dialogues.

No One Knows the State of the Art in Geospatial Foundation Models Earthdial: Turning multi-sensory earth observations to interactive dialogues

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.843761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41de7d3f-7946-4eda-be8c-d0dd9fcd4366 · outbound

This paper cites Beyond the imitation game: Quanti- fying and extrapolating the capabilities of language models.Transactions on Machine Learning Research.

No One Knows the State of the Art in Geospatial Foundation Models Beyond the imitation game: Quanti- fying and extrapolating the capabilities of language models.Transactions on Machine Learning Research

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.721210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8cb6e911-df06-4b64-930d-23653afea6ae · outbound

This paper cites Torchgeo: deep learning with geospatial data.ACM Transactions on Spatial Algorithms and Systems, 11(4):1–28.

No One Knows the State of the Art in Geospatial Foundation Models Torchgeo: deep learning with geospatial data.ACM Transactions on Spatial Algorithms and Systems, 11(4):1–28

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.713917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a3a5d7a0-6cee-4e72-9f1c-eb029e6f4d2a · outbound

This paper cites Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery.ISPRS Journal of Photogrammetry and Remote Sensing, 184:116–130.

No One Knows the State of the Art in Geospatial Foundation Models Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery.ISPRS Journal of Photogrammetry and Remote Sensing, 184:116–130

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.794322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 140d2877-0aaa-47af-9a23-f80baab81554 · outbound

This paper cites an unresolved cited work.

No One Knows the State of the Art in Geospatial Foundation Models Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-05-15T12:40:36.748718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c8a992ce-b45a-40ff-87bd-224e32829c47 · outbound

This paper cites Galileo: Learning global & local features of many remote sensing modalities.

No One Knows the State of the Art in Geospatial Foundation Models Galileo: Learning global & local features of many remote sensing modalities

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.726153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 62cd4bec-f4eb-42e1-a93f-d3c879def313 · outbound

This paper cites Panopticon: Advancing any-sensor foundation models for earth observation.

No One Knows the State of the Art in Geospatial Foundation Models Panopticon: Advancing any-sensor foundation models for earth observation

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.772545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cdc897a6-1205-406f-9fc5-b4a4252ceca5 · outbound

This paper cites Harnessing massive satellite imagery with efficient masked image modeling.

No One Knows the State of the Art in Geospatial Foundation Models Harnessing massive satellite imagery with efficient masked image modeling

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.735448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c420ada-3e09-46ac-8cb9-25e75233ca83 · outbound

This paper cites an unresolved cited work.

No One Knows the State of the Art in Geospatial Foundation Models Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-05-15T12:40:36.733224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f29a37d7-5351-4f3f-9e97-788ef4ee6482 · outbound

This paper cites Aid: A benchmark data set for performance evaluation of aerial scene classification.IEEE Transactions on Geoscience and Remote Sensing, 55(7):3965–3981.

No One Knows the State of the Art in Geospatial Foundation Models Aid: A benchmark data set for performance evaluation of aerial scene classification.IEEE Transactions on Geoscience and Remote Sensing, 55(7):3965–3981

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.739759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9fbfabfe-3fd3-4d4a-bc16-099b5792a92c · outbound

This paper cites Dota: A large-scale dataset for object detection in aerial images.

No One Knows the State of the Art in Geospatial Foundation Models Dota: A large-scale dataset for object detection in aerial images

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.742008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a15d6485-3688-4403-8600-da91ed7d9d33 · outbound

This paper cites Foundation models for remote sensing and earth observation: A survey.IEEE Geoscience and Remote Sensing Magazine.

No One Knows the State of the Art in Geospatial Foundation Models Foundation models for remote sensing and earth observation: A survey.IEEE Geoscience and Remote Sensing Magazine

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.723609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ff56765-c5a7-43c3-84b7-f0b018b91a42 · outbound

This paper cites Xiong, Y.

No One Knows the State of the Art in Geospatial Foundation Models Xiong, Y

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Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:19:28.527792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1510cdeb-fe04-4e83-aa06-a1c43645bf32 · outbound

This paper cites Bag-of-visual-words and spatial extensions for land-use classifi- cation.

No One Knows the State of the Art in Geospatial Foundation Models Bag-of-visual-words and spatial extensions for land-use classifi- cation

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.826560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae39d87d-950e-411e-b00f-e4d58d9f1a54 · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

No One Knows the State of the Art in Geospatial Foundation Models A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

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Resolution: verified exact; arxiv_id, observed 2026-05-17T22:12:05.852900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 204bfb40-b622-4ae8-8faa-29d00d2271cd · outbound

This paper cites Ctxmim: Context-enhanced masked image modeling for remote sensing image understanding.ACM Transactions on Multimedia Computing, Communications and Applications, 21(12):1–22.

No One Knows the State of the Art in Geospatial Foundation Models Ctxmim: Context-enhanced masked image modeling for remote sensing image understanding.ACM Transactions on Multimedia Computing, Communications and Applications, 21(12):1–22

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.836021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88ba455b-d364-4db2-8b30-469cd9f33c30 · outbound

This paper cites Earthgpt: A universal multimodal large language model for multisensor image comprehension in remote sensing domain.IEEE Transactions on Geoscience and Remote Sensing, 62:1–20.

No One Knows the State of the Art in Geospatial Foundation Models Earthgpt: A universal multimodal large language model for multisensor image comprehension in remote sensing domain.IEEE Transactions on Geoscience and Remote Sensing, 62:1–20

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.855103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4073f9a6-a8b0-4718-9332-c55cf166f5b4 · outbound

This paper cites Rs5m and georsclip: A large- scale vision-language dataset and a large vision-language model for remote sensing.IEEE Transactions on Geoscience and Remote Sensing, 62:1–23.

No One Knows the State of the Art in Geospatial Foundation Models Rs5m and georsclip: A large- scale vision-language dataset and a large vision-language model for remote sensing.IEEE Transactions on Geoscience and Remote Sensing, 62:1–23

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:40:36.860146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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