Exploring Unsupervised Domain Adaptation Methods for Automated Linear Disturbance Mapping

In the Canadian boreal forest region habitat fragmentation due to linear disturbances (roads, seismic explora-tion, pipelines, and energy transmission corridors) is a leading cause for the decline of wood-land caribou (Rangifer tarandus) – boreal population; and as a result, a deep understanding of linear disturbances (amount, spatial distribution, dynamics) has become a research and forest management priority in Canada. An ideal tool in mitigating and restoring the impact of linear disturbances is a way to automatically generate maps detailing forest region habitat fragmentation. As a result, the focus of this project is to develop machine learning ap-proaches to automatically create maps of linear disturbances, where these maps are produced from satellite data covering the area of interest.

Faculty Supervisor:

Christopher D Storie

Student:

Partner:

Hatfield Consultants

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Winnipeg

Program: