DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets

1 code implementation • 17 Oct 2024 • Elena Sierra, Lauren E. Gillespie, Salim Soltani, Moises Exposito-Alonso, Teja Kattenborn

Here we introduce Diversity Shift (DivShift), a framework for quantifying the effects of domain-specific distribution shifts on machine learning model performance.

Deep Learning Fine-Grained Image Classification

Earth System Data Cubes: Avenues for advancing Earth system research

no code implementations • 5 Aug 2024 • David Montero, Guido Kraemer, Anca Anghelea, César Aybar, Gunnar Brandt, Gustau Camps-Valls, Felix Cremer, Ida Flik, Fabian Gans, Sarah Habershon, Chaonan Ji, Teja Kattenborn, Laura Martínez-Ferrer, Francesco Martinuzzi, Martin Reinhardt, Maximilian Söchting, Khalil Teber, Miguel D. Mahecha

These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues.

OpenForest: A data catalogue for machine learning in forest monitoring

1 code implementation • 1 Nov 2023 • Arthur Ouaknine, Teja Kattenborn, Etienne Laliberté, David Rolnick

These datasets are grouped in OpenForest, a dynamic catalogue open to contributions that strives to reference all available open access forest datasets.