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2019 | OriginalPaper | Chapter

Evaluation of Deep Species Distribution Models Using Environment and Co-occurrences

Authors : Benjamin Deneu, Maximilien Servajean, Christophe Botella, Alexis Joly

Published in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Publisher: Springer International Publishing

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Abstract

This paper presents an evaluation of several approaches of plants species distribution modeling based on spatial, environmental and co-occurrences data using machine learning methods. In particular, we re-evaluate the environmental convolutional neural network model that obtained the best performance of the GeoLifeCLEF 2018 challenge but on a revised dataset that fixes some of the issues of the previous one. We also go deeper in the analysis of co-occurrences information by evaluating a new model that jointly takes environmental variables and co-occurrences as inputs of an end-to-end network. Results show that the environmental models are the best performing methods and that there is a significant amount of complementary information between co-occurrences and environment. Indeed, the model learned on both inputs allows a significant performance gain compared to the environmental model alone.

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previous chapter An Ensemble Approach to Cross-Domain Authorship Attribution

next chapter Interactive Learning-Based Retrieval Technique for Visual Lifelogging

https://github.com/maximiliense/GLC19.

https://pytorch.org/.

http://scikit-learn.org/stable/.

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Title: Evaluation of Deep Species Distribution Models Using Environment and Co-occurrences
Authors: Benjamin Deneu
Maximilien Servajean
Christophe Botella
Alexis Joly
Publisher: Springer International Publishing
Book: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-030-28576-0

Electronic ISBN: 978-3-030-28577-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-28577-7_18

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