Most vision architectures assume that observations lie on a regular grid, an effective abstraction for natural images but a restrictive one for sensing data whose channels, temporal sampling, spatial resolution, and geometry can vary. Generic set-based architectures remove the grid, but also remove useful spatial induc...
Hugo Riffaud de Turckheim, Sylvain Lobry, Nicolas Houdré et al.· 0 citations
These findings identify the practical regime in which pretrained representations are most valuable and suggest that instance discrimination, rather than forest semantics, is the main remaining obstacle to a general-purpose 3D forest foundation model.
Yuan-Wen Yue, Stefano Puliti, Damien Robert et al.· 0 citations
A Sampling-Aware Global Evaluation (SAGE) benchmark is introduced, combining GBIF records for training with sPlotOpen vegetation plots for presence-absence evaluation across 5771 plant species, and an evaluation framework that groups species based on two properties, sampling effort and relative prevalence is proposed.
Emilia Arens, Nina Van Tiel, Robin Zbinden et al.· 0 citations
This paper presents a concise, end-to-end account of the recommended practices spanning the pipeline from satellite data to an operational map product, organized around six interconnected themes: the EO data infrastructure landscape, data selection and preprocessing, ML dataset construction and model training, uncertai...
Ghjulia Sialelli, Robin Young, Yu-Chang Jiang et al.· arXiv.org· 1 citation
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