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Found 7 papers, 3 papers with code
Fully automatic extraction of morphological traits from the Web: utopia or reality?
Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem.
GeoPlant: Spatial Plant Species Prediction Dataset
The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological knowledge and conservation efforts.
Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?
This strategy estimates user expertise as a trust score per user based on their ability to identify plant species from crowdsourced data.
Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts
Cross-validation yields average accuracies of 0. 61 for status classification and 0. 78 for binary classification.
AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale
In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island.
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
We designed a European scale dataset covering around ten thousand plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~ten meters), and their spatial transferability.
The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in conservation.
