Species recommendation using intensity models and sampling bias correction (GeoLifeCLEF2019: Lof of Lof team)

Abstract : This paper presents three algorithms for species spatial rec-ommendation in the context of the GeoLifeCLEF 2019 challenge. Wesubmitted three runs to this task, all based on the estimation of speciesenvironmental intensities through Poisson processes models: The first isdirectly derived from MAXENT method used for species distributionmodels. The second method is a modification that uses sites were speciesobserved as background points in MAXENT to correct for spatial sam-pling bias due to heterogeneous sampling in the training occurrences. Thelast method jointly estimates species and sampling intensities to correctfor sampling bias. The best run was the MAXENT method which wasranked 14 over 44 runs with a top30 accuracy of 0.111 on the test setwhile the worst performing method was LOF with an accuracy of 0.086(ranked 19)
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https://hal.umontpellier.fr/hal-02288944
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Submitted on : Monday, September 16, 2019 - 11:34:37 AM
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Pascal Monestiez, Christophe Botella. Species recommendation using intensity models and sampling bias correction (GeoLifeCLEF2019: Lof of Lof team). CLEF 2019: Conference and Labs of the Evaluation Forum, Sep 2019, Lugano, Switzerland. pp.1-8. ⟨hal-02288944⟩

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