Location-Based Plant Species Prediction Using A CNN Model Trained On Several Kingdoms - Best Method Of GeoLifeCLEF 2019 Challenge

International audienceThis technical report describes the model that achieved the best performance of the GeoLifeCLEF challenge, the objective of which was to evaluate methods for plant species prediction based on their geographical location. Our method is based on an adaptation of the Inception v3 architecture initially dedicated to the classification of RGB images. We modified the input layer of this architecture so as to process the spatialized environmental tensors as images with 77 distinct channels. Using this architecture, we did train several models that mainly differed in the used training data and in the predicted output classes. One of the main objective, in particular, was to compare the performance of a model trained with plant occurrences only to that obtained with a model trained on all available occurrences, including the species of other kingdoms. Our results show that the global model performs consistently better than the plant-specific model. This suggests that the convolutional neural network is able to capture some inter-dependencies among all species and that this information significantly improves the generalisation capacity of the model for any species

Location-Based Plant Species Prediction Using A CNN Model Trained On Several Kingdoms - Best Method Of GeoLifeCLEF 2019 Challenge

International audienceThis technical report describes the model that achieved the best performance of the GeoLifeCLEF challenge, the objective of which was to evaluate methods for plant species prediction based on their geographical location. Our method is based on an adaptation of the Inception v3 architecture initially dedicated to the classification of RGB images. We modified the input layer of this architecture so as to process the spatialized environmental tensors as images with 77 distinct channels. Using this architecture, we did train several models that mainly differed in the used training data and in the predicted output classes. One of the main objective, in particular, was to compare the performance of a model trained with plant occurrences only to that obtained with a model trained on all available occurrences, including the species of other kingdoms. Our results show that the global model performs consistently better than the plant-specific model. This suggests that the convolutional neural network is able to capture some inter-dependencies among all species and that this information significantly improves the generalisation capacity of the model for any species

Overview of GeoLifeCLEF 2019: plant species prediction using environment and animal occurrences

International audienceThe GeoLifeCLEF challenge aim to evaluate location-based species recommendation algorithms through open and perennial datasets in a reproducible way. It offers a ground for large-scale geographic species prediction using cross-kingdom occurrences and spatialized environmental data. The main novelty of the 2019 campaign over the previous one is the availability of new occurrence datasets: (i) automatically identified plant occurrences coming from the popular Pl@ntnet platform and (ii) animal occurrences coming from the GBIF platform. This paper presents an overview of the resources and assessment of the GeoLifeCLEF 2019 task, synthesizes the approaches used by the participating groups and analyzes the main evaluation results. We highlight new successful approaches relevant for community modeling like models learning to predict occurrences from many biological groups and methods weighting occurrences based on species infrequency