Investigating Different Geo Priors for Image Classification
This work addresses improving species classification accuracy for ecological research by integrating spatial data, but it appears incremental as it focuses on evaluating existing SINR models rather than introducing new methods.
The study evaluated Spatial Implicit Neural Representations (SINR) models as geographical priors for species classification from iNaturalist images, analyzing model configurations and handling of untrained species to identify effectiveness factors.
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.