deep learning-based solutions

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