Machine learning can be applied to many fields as land cover classification from remote sensing imagery. The performance and accuracy of classification will depend on the number of raster bands, the image resolution, the land cover type and the algorithm used. This webinar will perform an applied case of land cover classification from a panchromatic image in Python using the Naives Bayes algorithm implemented on the Scikit Learn package. The classification will cover four categories as: rivers, river beaches, woods and pastures; coding is performed under a Jupyter Notebook with Python running from a geospatial Conda environment. Some graphics and statistics about the classification precision are also included on the webinar.
IMPORTANT: You will need a conda environment with geospatial tools for the webinar. Create the environment following this link: hatarilabs.com/ih-en/how-to-install-python-geopandas-in-windows-on-a-conda-environment-tutorial
Intructor
Saul Montoya M.Sc
Hydrogeologist - Numerical Modeler
Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.
Language
English
Event date
Tuesday, January 25, 2022 6:00 p.m. New York Time (EST)
Register
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