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The GeoLifeCLEF 2020 Dataset [article]

Elijah Cole, Benjamin Deneu, Titouan Lorieul, Maximilien Servajean, Christophe Botella, Dan Morris, Nebojsa Jojic, Pierre Bonnet, Alexis Joly
2020 arXiv   pre-print
We also discuss the GeoLifeCLEF 2020 competition, which aims to use this dataset to advance the state-of-the-art in location-based species recommendation.  ...  To facilitate research in this area, we present the GeoLifeCLEF 2020 dataset, which consists of 1.9 million species observations paired with high-resolution remote sensing imagery, land cover data, and  ...  These values are based on machine learning models trained on around 150,000 soil profiles around the world and 158 remote-sensing based covariates.  ... 
arXiv:2004.04192v1 fatcat:rdlcymmkqvbcfmg2p6knxhkwbu

Evaluation of Deep Species Distribution Models using Environment and Co-occurrences [article]

Benjamin Deneu , Christophe Botella
2019 arXiv   pre-print
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.  ...  Indeed, the model learned on both inputs allows a significant performance gain compared to the environmental model alone.  ...  In particular we used the random forest algorithm implemented within the scikit-learn framework 3 .  ... 
arXiv:1909.08825v1 fatcat:imyfon2v6bcxremr35sjhao2l4

Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction [chapter]

Alexis Joly, Hervé Goëau, Stefan Kahl, Benjamin Deneu, Maximillien Servajean, Elijah Cole, Lukáš Picek, Rafael Ruiz de Castañeda, Isabelle Bolon, Andrew Durso, Titouan Lorieul, Christophe Botella (+6 others)
2020 Lecture Notes in Computer Science  
recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data, and (iv) SnakeCLEF: snake identification based on image and geographic  ...  Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation.  ...  On the contrary, the method used in LIRMM/Inria Run 1 was based solely on the punctual environmental variables using a machine learning method classically used for species distribution models (Random Forest  ... 
doi:10.1007/978-3-030-58219-7_23 fatcat:2bpxx4tq7rfrdke27nuyq3plii

Exploiting Social Networks. Technological Trends (Habilitation Thesis) [article]

Adrian Iftene
2020 arXiv   pre-print
S., Iftene, A . (2019) Location-Based Species Recommendation -GeoLifeCLEF 2019 Challenge .  ...  Participants use techniques in the area of artificial intelligence, such as machine learning, neural networks, deep learning, etc.  ... 
arXiv:2004.14386v1 fatcat:faoyxt42e5b5no57v6r5arnu5a