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Paper Citation Record · LEDGER
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models
As of 8 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2606.02374.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
60 of 60 outbound references displayed
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External citation measurements
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Outbound references
Observation 874bbaec-541a-42a8-8a17-212b0662c306 · outbound
This paper cites Pix2poly: A sequence prediction method for end-to-end polygonal building footprint extraction from remote sensing imagery.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Pix2poly: A sequence prediction method for end-to-end polygonal building footprint extraction from remote sensing imagery
Reference 1
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This paper cites an unresolved cited work.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Unresolved cited work
Reference 2
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This paper cites Geolink: Empow- ering remote sensing foundation model with openstreetmap data.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Geolink: Empow- ering remote sensing foundation model with openstreetmap data
Reference 3
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Observation 8f55ef08-cf64-48cc-8e4c-eab82686bcbb · outbound
This paper cites City foundation models for learning general purpose repre- sentations from openstreetmap.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models City foundation models for learning general purpose repre- sentations from openstreetmap
Reference 4
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Observation d7ce2a03-ce2a-4308-b3bf-938b4992d440 · outbound
This paper cites Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation 7099b8bf-181d-4c04-b1d1-b0af7e4bb7e3 · outbound
This paper cites AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
Reference 6
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Source-reported events for the cited work
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Observation b040ca3e-47a2-4775-85cf-64847f5171c5 · outbound
This paper cites Geoclip: Clip-inspired alignment be- tween locations and images for effective worldwide geo- localization, 2023.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Geoclip: Clip-inspired alignment be- tween locations and images for effective worldwide geo- localization, 2023
Reference 7
- Resolution
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Observation 147640c3-f587-4c90-9031-a7c57be1008f · outbound
This paper cites S2vec: Self-supervised geospatial embed- dings for the built environment.ACM Transactions on Spa- tial Algorithms and Systems, 2026.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models S2vec: Self-supervised geospatial embed- dings for the built environment.ACM Transactions on Spa- tial Algorithms and Systems, 2026
Reference 8
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Source-reported events for the cited work
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Observation 562518be-8a5b-4b8f-a0dc-94ff661fb9eb · outbound
This paper cites Geo2vec: Shape-and distance-aware neural representation of geospatial entities.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Geo2vec: Shape-and distance-aware neural representation of geospatial entities
Reference 9
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Observation 6b4f2894-dca8-4d53-a194-80b8989484eb · outbound
This paper cites Satmae: Pre-training transformers for tem- poral and multi-spectral satellite imagery.Advances in Neu- ral Information Processing Systems, 35:197–211, 2022.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Satmae: Pre-training transformers for tem- poral and multi-spectral satellite imagery.Advances in Neu- ral Information Processing Systems, 35:197–211, 2022
Reference 10
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Source-reported events for the cited work
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Observation 028d0581-9a10-439f-9ad2-9b91dbb5162e · outbound
This paper cites Bo- janowski.
Reference 11
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Observation 108279a4-b205-4fc0-b8e8-4dbdd83804df · outbound
This paper cites Terrafm: A scalable foundation model for unified multisensor earth observation.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Terrafm: A scalable foundation model for unified multisensor earth observation
Reference 12
- Resolution
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Observation f594d2cc-05a2-4cd9-817c-e89a94f98b35 · outbound
This paper cites Geobind: Binding text, image, and audio through satellite images.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Geobind: Binding text, image, and audio through satellite images
Reference 13
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Observation 9a570c2f-d5bb-42ca-b4f6-5c1103152ba4 · outbound
This paper cites Range: Retrieval aug- mented neural fields for multi-resolution geo-embeddings.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Range: Retrieval aug- mented neural fields for multi-resolution geo-embeddings
Reference 14
- Resolution
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Observation 96084082-f3ea-4870-bbc9-1a88e7d7835a · outbound
This paper cites Climplicit: Climatic implicit embeddings for global ecological tasks.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Climplicit: Climatic implicit embeddings for global ecological tasks
Reference 15
- Resolution
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Observation add0e8fc-2608-4b7c-9f67-a16096e0bc9f · outbound
This paper cites Learning to model the world: A survey of world models in artificial intelligence.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Learning to model the world: A survey of world models in artificial intelligence
Reference 16
- Resolution
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Observation b4c5f0e6-a85c-42c8-88f1-10453aecea28 · outbound
This paper cites Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 6ccdca59-d0f5-40bd-ac01-bcc5cc8d8c1a · outbound
This paper cites Asynchronous Remote Sensing Time-Series Fusion for Cloud Removal and Anytime Reconstruction.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Asynchronous Remote Sensing Time-Series Fusion for Cloud Removal and Anytime Reconstruction
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation 1beb4e30-bfbc-4251-98d7-aaac0d7d122b · outbound
This paper cites TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
Reference 19
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Observation ec7b41a5-f132-445e-8029-fcf92918dd96 · outbound
This paper cites Forgaard, J.
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation 1d37e823-83d1-42d3-a93d-2301ed784db6 · outbound
This paper cites A survey of uncertainty in deep neural networks.Artificial Intelligence Review, 56, 2023.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models A survey of uncertainty in deep neural networks.Artificial Intelligence Review, 56, 2023
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- Resolution
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Observation 9a26da7c-093d-4a56-86d7-0ae030216074 · outbound
This paper cites Belenguer-Plomer, Kennedy Adriko, Paolo Fraccaro, Romeo Kienzler, Rania Briq, Sab- rina Benassou, Michele Lazzarini, and Conrad M.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Belenguer-Plomer, Kennedy Adriko, Paolo Fraccaro, Romeo Kienzler, Rania Briq, Sab- rina Benassou, Michele Lazzarini, and Conrad M
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- Resolution
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Source-reported events for the cited work
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Observation a0385dcf-8f46-4685-a07e-be06d47c180f · outbound
This paper cites Shrug-fm: Reliability-aware foundation models for earth observation.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Shrug-fm: Reliability-aware foundation models for earth observation
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- Resolution
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Observation db7b57c3-17df-41b3-85b3-40ae48c316bc · outbound
This paper cites Reasoning with language model is planning with world model.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Reasoning with language model is planning with world model
Reference 24
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 462e34f0-5bb4-4010-b842-7207dac69da5 · outbound
This paper cites Spectralgpt: Spectral remote sensing foun- dation model.IEEE transactions on pattern analysis and machine intelligence, 46(8):5227–5244, 2024.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Spectralgpt: Spectral remote sensing foun- dation model.IEEE transactions on pattern analysis and machine intelligence, 46(8):5227–5244, 2024
Reference 25
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Source-reported events for the cited work
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Observation 7b67e460-17e0-4b99-80fe-391eaf15494a · outbound
This paper cites Terramind: Large-scale generative mul- timodality for earth observation.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Terramind: Large-scale generative mul- timodality for earth observation
Reference 26
- Resolution
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Unavailable: canonical work link unavailable.
Observation 4f482b71-32aa-4913-8ed5-5f826d9fb51d · outbound
This paper cites an unresolved cited work.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Unresolved cited work
Reference 27
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Observation 83a90d30-08f0-42c7-acb9-76e414f640ba · outbound
This paper cites Satclip: Global, general- purpose location embeddings with satellite imagery.Pro- ceedings of the AAAI Conference on Artificial Intelligence, 39(4):4347–4355, 2025.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Satclip: Global, general- purpose location embeddings with satellite imagery.Pro- ceedings of the AAAI Conference on Artificial Intelligence, 39(4):4347–4355, 2025
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This paper cites Geoai for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography.ISPRS International Journal of Geo-Information, 11(7):385, 2022.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Geoai for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography.ISPRS International Journal of Geo-Information, 11(7):385, 2022
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This paper cites Beyond alphaearth: Toward human-centered geospatial foundation models via poi-guided contrastive learning.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Beyond alphaearth: Toward human-centered geospatial foundation models via poi-guided contrastive learning
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1dbee145-6998-4633-81b1-ce718a12397c · outbound
This paper cites GAIR: Location-aware self-supervised con- trastive pre-training with geo-aligned implicit representa- tions.ISPRS Journal of Photogrammetry and Remote Sens- ing, 237:166–182, 2026.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models GAIR: Location-aware self-supervised con- trastive pre-training with geo-aligned implicit representa- tions.ISPRS Journal of Photogrammetry and Remote Sens- ing, 237:166–182, 2026
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Observation cf663238-e7de-4b3c-9dca-40ee43ac98be · outbound
This paper cites Towards general-purpose representation learning of polygonal geometries.GeoInformatica, 27(2):289–340,.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Towards general-purpose representation learning of polygonal geometries.GeoInformatica, 27(2):289–340,
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Observation d31a6a55-cc2e-4257-8bb9-2d1db6730984 · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Csp: Self-supervised contrastive spatial pre- training for geospatial-visual representations
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Unavailable: canonical work link unavailable.
Observation fb617fc1-f269-428d-ae78-8bb2f4efc3fc · outbound
This paper cites On the opportunities and challenges of foundation mod- els for geoai (vision paper).ACM Transactions on Spatial Algorithms and Systems, 10(2):1–46, 2024.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models On the opportunities and challenges of foundation mod- els for geoai (vision paper).ACM Transactions on Spatial Algorithms and Systems, 10(2):1–46, 2024
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Srl: Towards a general-purpose framework for spatial representation learn- ing
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Observation 730fc5d4-fb0d-43d4-9235-2cc526605807 · outbound
This paper cites Towards the next generation of geospatial artificial in- telligence.International Journal of Applied Earth Observa- tion and Geoinformation, 136:104368, 2025.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Towards the next generation of geospatial artificial in- telligence.International Journal of Applied Earth Observa- tion and Geoinformation, 136:104368, 2025
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Pangaea: A global and inclusive benchmark for geospatial foundation models
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This paper cites Reed, Ritwik Gupta, Shufan Li, Sarah Brock- man, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Reed, Ritwik Gupta, Shufan Li, Sarah Brock- man, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell
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Observation a28d7585-5470-433b-add1-7fb5f6537f5e · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Unresolved cited work
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This paper cites Geographic location encoding with spherical harmonics and sinusoidal representation networks.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Geographic location encoding with spherical harmonics and sinusoidal representation networks
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Observation d638c6ea-45f4-44fe-b7b8-66f6ed02a9d6 · outbound
This paper cites Taxabind: A unified embedding 8 space for ecological applications.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Taxabind: A unified embedding 8 space for ecological applications
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Unresolved cited work
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Observation c290cbf2-155d-4bcb-879a-ad140259caa3 · outbound
This paper cites Exebench: Benchmarking foundation models on extreme earth events.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Exebench: Benchmarking foundation models on extreme earth events
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Observation 22f6e9e3-9789-4ca4-bab0-62eed81185ca · outbound
This paper cites Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications
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- Resolution
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Source-reported events for the cited work
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Observation 7f25eacf-230f-4d10-8957-4cb8efdaf3b9 · outbound
This paper cites Prithvi-eo-2.0: A versatile multi-temporal foun- dation model for earth observation applications.IEEE Trans- actions on Geoscience and Remote Sensing, 2025.
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Prithvi-eo-2.0: A versatile multi-temporal foun- dation model for earth observation applications.IEEE Trans- actions on Geoscience and Remote Sensing, 2025
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Observation a4d81b7c-4782-4c3e-9802-2faa5b610a84 · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Green, Evan Shelhamer, Hannah Kerner, and David Rolnick
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Vargas-Munoz, Shivangi Srivastava, Devis Tuia, and Alexandre X
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This paper cites an unresolved cited work.
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Reference 48
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Reference 49
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Reference 50
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Reobench: Benchmarking ro- bustness of earth observation foundation models
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Fairness by “where”: A statistically-robust and model-agnostic bi-level learning framework
Reference 53
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Observation ab5df8ff-a948-4d24-ac81-f702282b5634 · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Stewart, Jie Zhao, Nils Lehmann, Thomas Dujardin, Zhenghang Yuan, Pedram Ghamisi, and Xiao Xiang Zhu
Reference 54
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Observation 5a23d563-888f-4fdb-bee9-f916a5c50f0f · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Poly- gongnn: Representation learning for polygonal geometries with heterogeneous visibility graph
Reference 55
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Observation 50ccfc57-8524-490b-8cf0-aaa9f04e1aaf · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Ot on the map: Quantify- ing domain shifts in geographic space
Reference 56
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Observation e7c405b5-aa63-42a6-b857-3e40907ac618 · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Eulerian neural network informed by chemical transport for air quality forecasting
Reference 57
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models Skysense v2: A unified foundation model for multi-modal remote sensing
Reference 58
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Observation b6a4794c-d625-49cd-816a-b35d4a02e517 · outbound
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models A satellite foundation model for improved wealth monitoring
Reference 59
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Observation 1a5afd2b-6ff2-4796-b6ab-4d8b95992c1a · outbound
This paper cites an unresolved cited work.
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Reference 60
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Pith citing papers
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