Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-29T08:41:45.660534Z
Paper Citation Record · LEDGER
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 4 inbound Pith citation observations for arXiv:2605.29330.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T09:29:28.484774Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-07T23:34:19.609467Z
Reference resolution
45 of 45 outbound references displayed
- verified exact5
- verified fuzzy0
- unresolved38
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 42c2b6e5-87f5-42df-8db8-29aa7c4c72e1 · outbound
This paper cites Domain adaptation for the classification of remote sensing data: An overview of recent advances.IEEE Geoscience and Remote Sensing Magazine, 4(2):41–57, 2016.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Domain adaptation for the classification of remote sensing data: An overview of recent advances.IEEE Geoscience and Remote Sensing Magazine, 4(2):41–57, 2016
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4325be76-e56e-439c-b395-a575cc08d42d · outbound
This paper cites Position: Mission critical–satellite data is a distinct modality in machine learning.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Position: Mission critical–satellite data is a distinct modality in machine learning
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fdaaa79a-887e-41b1-919b-7aa560f1b5e3 · outbound
This paper cites Geo-bench-2: From performance to capability, rethinking evaluation in geospatial ai, 2026.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Geo-bench-2: From performance to capability, rethinking evaluation in geospatial ai, 2026
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9f159a93-1e26-4cf1-988f-e895519b5ddc · outbound
This paper cites Benchmarking neural network robustness to common corruptions and perturbations.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Benchmarking neural network robustness to common corruptions and perturbations
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c2803a2-486e-46e2-8923-1d85ad3bab37 · outbound
This paper cites Temme, Jonas Rauber, Heiko H.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Temme, Jonas Rauber, Heiko H
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5ba349f-2304-41d0-9b5a-9dfb95287197 · outbound
This paper cites Towards deep learning models resistant to adversarial attacks.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Towards deep learning models resistant to adversarial attacks
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fcefb255-0336-4351-8f2f-9fc5f3d7d4ce · outbound
This paper cites Wichmann, and Wieland Brendel.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Wichmann, and Wieland Brendel
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4a9bbc1-f747-4ff2-9c30-38e5d875a63f · outbound
This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Arbitrary style transfer in real-time with adaptive instance normalization
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea159e34-8d5f-4b27-8727-72d317e098db · outbound
This paper cites Domain-adversarial training of neural networks.Journal of Machine Learning Research, 17(59):1–35, 2016.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Domain-adversarial training of neural networks.Journal of Machine Learning Research, 17(59):1–35, 2016
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b85f35f-a62e-400a-a54f-191cce7e58fd · outbound
This paper cites Deep coral: Correlation alignment for deep domain adaptation.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Deep coral: Correlation alignment for deep domain adaptation
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5baae8c-0ed7-4512-acf6-e29425d8d4f8 · outbound
This paper cites Invariant Risk Minimization.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Invariant Risk Minimization
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:43:14.862187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d658d9e2-a65d-44d8-8d3e-43aa5394d49e · outbound
This paper cites Tent: Fully test-time adaptation by entropy minimization.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Tent: Fully test-time adaptation by entropy minimization
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b35736c2-d854-4698-9097-276e3e37d8bf · outbound
This paper cites WILDS: A benchmark of in-the-wild distribution shifts.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation WILDS: A benchmark of in-the-wild distribution shifts
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bda73bd8-cae7-42f8-abae-96a77952d19b · outbound
This paper cites Evaluating machine accuracy on ImageNet.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Evaluating machine accuracy on ImageNet
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70f93729-b2e8-48d2-a797-8d6972327078 · outbound
This paper cites ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.Advances in Neural Information Processing Systems, 32, 2019.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.Advances in Neural Information Processing Systems, 32, 2019
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0ba486e-0224-490a-82b2-dddf6a2f4b38 · outbound
This paper cites an unresolved cited work.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Unresolved cited work
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a7e8c381-5817-42a3-833f-f56ec76a353d · outbound
This paper cites Measuring robustness to natural distribution shifts in image classification.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Measuring robustness to natural distribution shifts in image classification
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f3ff9161-9d27-4a35-9de4-6090a8d3e624 · outbound
This paper cites Assessing out-of-domain generalization for robust building damage detection.arXiv preprint, 2020.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Assessing out-of-domain generalization for robust building damage detection.arXiv preprint, 2020
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de512ca3-d8cb-490e-b9cf-df1f179f7b38 · outbound
This paper cites Multi-region transfer learning for segmen- tation of crop field boundaries in satellite images with limited labels.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Multi-region transfer learning for segmen- tation of crop field boundaries in satellite images with limited labels
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fb02a77-f812-4e9b-81e7-ca17d9ec0eb2 · outbound
This paper cites Geocrossbench: Cross-band generalization for remote sensing.arXiv preprint arXiv:2511.02831, 2025.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Geocrossbench: Cross-band generalization for remote sensing.arXiv preprint arXiv:2511.02831, 2025
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:43:14.859711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7d768e8b-9974-4dc1-83ae-5c917b365ec5 · outbound
This paper cites EarthNets: Empowering AI in Earth Observation.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation EarthNets: Empowering AI in Earth Observation
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:43:14.865599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d5728367-a859-4479-b382-8ac6e48164e5 · outbound
This paper cites Geo-bench: Toward foundation models for earth monitoring, 2023.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Geo-bench: Toward foundation models for earth monitoring, 2023
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72e8543d-8302-424a-84d3-445db8e7a3c3 · outbound
This paper cites Remote sensing image scene classification: Benchmark and state of the art.Proceedings of the IEEE, 105(10):1865–1883, 2017.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Remote sensing image scene classification: Benchmark and state of the art.Proceedings of the IEEE, 105(10):1865–1883, 2017
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 59089481-66ff-41c3-93ac-807af25586af · outbound
This paper cites Bag-of-visual-words and spatial extensions for land-use classifi- cation.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Bag-of-visual-words and spatial extensions for land-use classifi- cation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31e041c2-3cdb-4f5a-b4a6-21c31e5c5e16 · outbound
This paper cites Lavista Ferres, and Jennifer Marcus.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Lavista Ferres, and Jennifer Marcus
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5b92320-80c6-4ecc-b7fd-4ba845bde122 · outbound
This paper cites reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:43:14.871907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ba0520f6-95c9-4392-8943-778aac9b5dab · outbound
This paper cites Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1
Reference 27
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 448a6c47-9c2c-4bcb-9782-6b9ac7f9750a · outbound
This paper cites Do imagenet classifiers generalize to imagenet?, 2019.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Do imagenet classifiers generalize to imagenet?, 2019
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56d9ee6b-1d3e-486d-b64c-ed09d7011903 · outbound
This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Deepglobe 2018: A challenge to parse the earth through satellite images
Reference 29
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2af20e2b-2605-49aa-ab29-2362330c52f6 · outbound
This paper cites Data fusion contest 2022 (dfc2022), January 2022.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Data fusion contest 2022 (dfc2022), January 2022
Reference 30
- Resolution
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- no resolver link, observed 2026-06-29T08:41:45.660534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 801c2ace-4430-4bf5-82f5-34bbac9c8a2a · outbound
This paper cites Xiong, Y.
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6ae9f108-6d93-4bb0-986b-c1998a77591f · outbound
This paper cites an unresolved cited work.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Unresolved cited work
Reference 32
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8ee01a43-c5d3-42dd-bbb2-dd34cdc0aa93 · outbound
This paper cites Clay foundation model: An open source ai model for earth, 2024.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Clay foundation model: An open source ai model for earth, 2024
Reference 33
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 99de9d4d-5787-4a01-9e7c-3b2f7a46aba3 · outbound
This paper cites Prithvi-eo-2.0: A versatile multi-temporal foundation model for earth observation applications, 2025.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Prithvi-eo-2.0: A versatile multi-temporal foundation model for earth observation applications, 2025
Reference 34
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f65703a5-b854-4f77-9b37-866e83d226ab · outbound
This paper cites TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 01362f99-d7de-442c-8621-4d998edcaca9 · outbound
This paper cites an unresolved cited work.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Unresolved cited work
Reference 36
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87ab23bc-4e66-46e3-b7a4-0ca03f879f46 · outbound
This paper cites Green, Evan Shelhamer, Hannah Kerner, and David Rolnick.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Green, Evan Shelhamer, Hannah Kerner, and David Rolnick
Reference 37
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93586ee2-ac60-4a01-83d7-80c035821dcd · outbound
This paper cites Terramind: Large-scale generative multimodality for earth observation, 2025.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Terramind: Large-scale generative multimodality for earth observation, 2025
Reference 38
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e36f3520-65ea-456b-8bb9-c21398a7eee1 · outbound
This paper cites Deep residual learning for image recognition, 2015.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Deep residual learning for image recognition, 2015
Reference 39
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 163f7cde-bb09-49ed-9983-84b114227097 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Reference 40
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5471fa97-d0ab-4747-a4ec-403b2b82abad · outbound
This paper cites Ramesh, Gabriel Goh, Sandish Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Ramesh, Gabriel Goh, Sandish Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever
Reference 41
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e695f91-25b6-47b7-b496-acd84a33d45e · outbound
This paper cites Decoupled weight decay regularization.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Decoupled weight decay regularization
Reference 42
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 33b492f9-03ef-4c75-b52a-a4ab6a090c54 · outbound
This paper cites Imagenet: A large- scale hierarchical image database.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Imagenet: A large- scale hierarchical image database
Reference 43
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d1592010-0ab3-431b-aea9-5b6f8686911a · outbound
This paper cites an unresolved cited work.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation Unresolved cited work
Reference 44
- Resolution
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Observation c933240f-c04f-448a-b8d7-4f48ae515693 · outbound
This paper cites licence ouverte.
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation licence ouverte
Reference 45
- Resolution
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Unavailable: canonical work link unavailable.
Pith citing papers
Observation 08ed879a-0615-4013-9bbe-cf52b1f5d0ec · inbound
Probing Geospatial SSL Representations with Environmental Signals cites this paper.
Probing Geospatial SSL Representations with Environmental Signals EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation f5533d45-e14a-4358-9269-fc3c64728160 · inbound
More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe cites this paper.
More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 0f8ec92a-80c8-4b96-a279-632f5c8b04b8 · inbound
Now We Know? A Systematic Comparison of TerraMind and THOR cites this paper.
Now We Know? A Systematic Comparison of TerraMind and THOR EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
Reference 41
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Observation 84d114f6-88eb-4873-8cde-c01244f102fc · inbound
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation cites this paper.
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.