Mississaugas of the Credit First Nation, Anishinaabeg Ethnobiology: Burial Sites and Biodiversity

Abstract

The present study provides a language-centered approach to a meta-analysis of Anishinaabe ethnobiology, and a burial mound detecting image recognition algorithm under the guidance of Anishinaabe place names. Ferrier lab collaborated with Mississaugas of the Credit First Nation, who is seeking to revitalize their culture, to make an attempt in understanding the Anishinaabe traditional way of living a good life through the lens of ethnobiology. Through quantitative study and possibility study, and intentional ambiguity to keep intellectual property within MCFN, traditionally used species are ranked. An image recognition algorithm was trained with maps retrieved from the Ontario Digital Terrain Model in TensorFlow, and successfully found ground elevation sites that are shaped like burial mounds. The study supports MCFN defending their land and water against proposed Highway 413 through data analysis, and will be used in protecting other culturally sensitive places.