Presenters-0714

CSDMS 2026: Modeling Landscapes in Motion

Using Machine Learning for Landscape Feature Detection: Beaver Dams and Beyond!

Emily Fairfax
University of Minnesota, United States
efairfax@umn.edu

Abstract
Image recognition is a powerful application of machine learning (ML) where computers can learn to automatically identify objects, patterns, and more. Meanwhile, there are enormous volumes of satellite imagery being collected every day with a variety of important landscape features readily visible. Though the name "image recognition" sounds like it's just based on visual data, modern ML methods allow many types of data to be included in the "image" - including full multispectral raster stacks and digital elevation models. If a data type can be converted to a raster, then ML image recognition can learn from it and recognize patterns in it. In this clinic, we will cover how to get started using ML to detect interesting landscape features in remotely sensed imagery using beaver dam identification as a case study.

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Of interest for:

Terrestrial Working Group

Hydrology Focus Research Group

Critical Zone Focus Research Group

Ecosystem Dynamics Focus Research Group

Artificial Intelligence & Machine Learning Initiative