Pith Number

pith:FXYRMFAB

pith:2026:FXYRMFAB36DGINVV2PB7LVIRUN

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GeoViSTA: Geospatial Vision-Tabular Transformer for Multimodal Environment Representation

Ashok Veeraraghavan, Guha Balakrishnan, Sadeer Al-Kindi, Yuhao Liu

GeoViSTA creates transferable geospatial embeddings by jointly modeling imagery and tabular socioeconomic data with cross-attention.

arxiv:2605.14406 v1 · 2026-05-14 · cs.LG · cs.CV

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Claims

C1strongest claim

jointly modeling the physical environment alongside structured socioeconomic context yields highly transferable representations for holistic geospatial inference

C2weakest assumption

That bilateral cross-attention and geography-aware attention can effectively align irregular tabular tokens with image patches and that the self-supervised masked autoencoding objective produces embeddings that generalize to downstream tasks without significant modality misalignment or information loss.

C3one line summary

GeoViSTA learns unified geospatial embeddings from co-registered imagery and tabular data via bilateral cross-attention and joint masked autoencoding, yielding better linear probing performance on mortality and fire hazard prediction tasks.

References

33 extracted · 33 resolved · 1 Pith anchors

[1] Skilful precipitation nowcasting using deep generative models of radar, 2021

[2] Downscaling Extreme Precipitation With Wasserstein Regularized Diffusion, 2025

[3] Designsafe: New cyberinfrastructure for natural hazards engineering, 2017

[4] Debris segmentation using post-hurricane aerial imagery,

[5] Air pollution and cardiovascular disease: Jacc state-of- the-art review, 2054

Formal links

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Receipt and verification

First computed	2026-05-17T23:39:07.423677Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2df1161401df866436b5d3c3f5d511a346592cae2b4c745d2aeb66ef3486f290

Aliases

arxiv: 2605.14406 · arxiv_version: 2605.14406v1 · doi: 10.48550/arxiv.2605.14406 · pith_short_12: FXYRMFAB36DG · pith_short_16: FXYRMFAB36DGINVV · pith_short_8: FXYRMFAB

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FXYRMFAB36DGINVV2PB7LVIRUN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2df1161401df866436b5d3c3f5d511a346592cae2b4c745d2aeb66ef3486f290

Canonical record JSON

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    "abstract_canon_sha256": "a3f24d165e293ea614d46f067faa4720a4c309373ca3f985c91362f2d891bd97",
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T05:46:07Z",
    "title_canon_sha256": "af49e00fa72ce8fba1ba8607891e98336b536c66f1c6a6363e844e6e4a1a412b"
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