Towards AI-powered global-scale species distribution models
0Estimating 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, we present recent work from my group on deep learning-based solutions for estimating species' ranges from incomplete data. We will also discuss some of the open challenges that exist in this space.
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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.