In this work we explore the feasibility of applying modern machine learning techniques to the invasive species distribution problem. One option would be using convolutional neural networks (CNNs) to analyze visual imagery. Advances in machine learning and the availability of high-resolution imagery (satellites, drones, etc.), has made monitoring species, forests and croplands using remote sensed data a viable option. This method also opens up the possibility of retrodiction—the interpretation of past events—by using a time series of satellite imagery. Coupled with epidemiological models, this method can help provide forecasts and analyze the different pathways by which invasive plants can spread and establish in this landscape.
Distinguished Professor in Biocomplexity, Biocomplexity Institute
Professor of Computer Science, School of Engineering and Applied Science