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Main Authors: Zhu, Angela, Lange, Christian, Hamilton, Max
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2508.15946
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author Zhu, Angela
Lange, Christian
Hamilton, Max
author_facet Zhu, Angela
Lange, Christian
Hamilton, Max
contents Species distribution models encode spatial patterns of species occurrence making them effective priors for vision-based species classification when location information is available. In this study, we evaluate various SINR (Spatial Implicit Neural Representations) models as a geographical prior for visual classification of species from iNaturalist observations. We explore the impact of different model configurations and adjust how we handle predictions for species not included in Geo Prior training. Our analysis reveals factors that contribute to the effectiveness of these models as Geo Priors, factors that may differ from making accurate range maps.
format Preprint
id arxiv_https___arxiv_org_abs_2508_15946
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Investigating Different Geo Priors for Image Classification
Zhu, Angela
Lange, Christian
Hamilton, Max
Computer Vision and Pattern Recognition
Species distribution models encode spatial patterns of species occurrence making them effective priors for vision-based species classification when location information is available. In this study, we evaluate various SINR (Spatial Implicit Neural Representations) models as a geographical prior for visual classification of species from iNaturalist observations. We explore the impact of different model configurations and adjust how we handle predictions for species not included in Geo Prior training. Our analysis reveals factors that contribute to the effectiveness of these models as Geo Priors, factors that may differ from making accurate range maps.
title Investigating Different Geo Priors for Image Classification
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2508.15946