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
Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models are typically specialized to a single task, sensor, and forest type, making adaptation expensive in terms of annotations, computation, and expertise. We ask whether a...
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
Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (ML) and large computing infrastructure. Although the barrier to generating such maps has dropped substantially, established best practices h...
Ghjulia Sialelli, Robin Young, Yu-Chang Jiang et al.· arXiv.org· 1 citation
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