Welcome! We are a team of scholars developing and applying machine learning methodology in service of ecology and conservation.

Blog posts

2023

How to Evaluate ML Models in Geospatial Settings?

6 minute read

Published:

Standard K-fold Cross-validation (KFCV) randomly divides a training set into K non-overlapping folds and iteratively holds out one fold at a time, training a model on the remainder (i.e., training folds) and measuring error on the held-out fold (i.e., validation fold). The average of these model errors across folds is the estimate of generalization performance for an unseen test set. KFCV provides unbiased performance estimates when applied to independent, identically distributed (iid) data. But does it also work well on geospatial data?

A comparison of remotely sensed environmental predictors for SDMs

6 minute read

Published:

There are many options when establishing environmntal variables from satellite imagery. First, one must select a satellite dataset (e.g., Landsat, MODIS, Sentinel). Second is deciding how to summarize the data (i.e., how to turn satellite imagery into input feature vectors). With the many satellite imagery datasets and methods of summarization, there is an open question of which environmental variables are best suited for SDMs. To help address this question, we compared the predictive power of several sets of environmental variables derived from Landsat satellite imagery in predicting 13 bird species across the state of Oregon, USA. This work was done in collaboration with the Oregon 2020 Project and was published in Landscape Ecology.