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Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance paper by Amandeep Kaur
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Paper

Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance

by Amandeep Kaur arxiv/2604.21104
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38.3 Top 10%
S: Semantic 50

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A: Authority 0
P: Popularity 0
R: Recency 79
Q: Quality 60
Tech Context
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New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Performance differences are largely attributed to the model architecture or input modalities, while the role of the pretraining dataset is rarely studied. To address this research gap, we conducted a systematic study on how the geographic composition of pretraining data affects a model's downstream performance. We created global and per-continen...

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Registry ID 2604.21104
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BibTeX 
@misc{arxiv_2604_21104,
  author = {Amandeep Kaur},
  title = {Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.21104}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Amandeep Kaur. (2026). Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance [Paper]. Free2AITools. https://arxiv.org/abs/2604.21104

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Semantic (S) 50

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Authority (A) 0
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Recency (R) 79
Quality (Q) 60

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FNI V2.0 for Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance: Authority (A:0), Popularity (P:0), Recency (R:79), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Performance differences are largely attributed to the model architecture or input modalities, while the role of the pretraining dataset is rarely studied. To address this research gap, we conducted a systematic study on how the geographic composition of pretraining data affects a model's downstream performance. We created global and per-continen..."

❝ Cite Node 

@article{Kaur2026Pretrain,
  title={Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance},
  author={Amandeep Kaur},
  journal={arXiv preprint arXiv:2604.21104},
  year={2026}
}

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Amandeep Kaur

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📅1970Published
⏱ī¸79RecencyFNI pillar
✅60QualityFNI pillar
🗂ī¸cs.CVField
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id
2604.21104
slug
2604.21104
source
arxiv
author
Amandeep Kaur
license
arXiv
tags
arxiv:cs.CV, arxiv:cs.LG

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