Towards AI-powered global-scale species distribution models

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  • Date
    26 June 2025
    Timeframe
    16:00 - 17:00 CEST Geneva
    Duration
    60 minutes
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    Estimating the geographic range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to multi-modal image and text data. In this talk, I will present recent work from my group on deep learning-based solutions for estimating species’ ranges from incomplete data. I will also discuss some of the open challenges that exist in this space.  

    Learning Objectives:  

    1. Understand the capabilities of current deep learning methods for species range estimation.
    2. Recognise the limitations of these models in the context of current open challenges in this space  

    Recommended Mastery Level / Prerequisites:  
    None