home Anil Madhavapeddy, Professor of Planetary Computing  

Species distribution modelling using CNNs

This is an idea proposed in 2023 as a Cambrige Computer Science Part III or MPhil project, and has been completed by Emily Morris. It was supervised by Anil Madhavapeddy and David A Coomes.

The goal of this project is to compare the performance of MaxEnt techniques to the performance of a CNN model for the task of species distribution modeling.

The CNN model will use remote sensing data as part of the input features. The remote sensing data we plan on using is a combination of LULC data (e.g. Dynamic World) and satellite imagery (Planet/Landsat 8/Sentinel 2). We will also use more classical environmental variables from WorldClim and soil data.

To evaluate it, we will focus on proteas for the species distribution modeling task. We have two observation data sets: the Protea Atlas and iNaturalist. The work for the CNN is largely based on the work done by Gillespie et al, who present a model that takes in an RGB image and an embedding for environment variables and predicts which species are present in the image. This method performs multispecies presence modeling and the use of other species is somewhat central to the method. Including other species gives training examples which are pseudo-absences for some species, circumventing the issue of the lack of negative data.

This project was conducted successfully, and presented at the CCAI Workshop at NeurIPS as 'Towards Scalable Deep Species Distribution Modelling using Global Remote Sensing'.

# 1st Jan 2023   iconideas ai cnns conservation idea-done idea-hard sdms sensing

Related News