Comparison of Machine Learning Land Use-Land Cover Supervised Classifiers Performance on Satellite Imagery Sentinel 2 using Lazy Predict Library
Abstract
The utilisation of various supervised classifier algorithms in classifying land use and land cover (LULC) from satellite imagery has been widely used worldwide, yet the implementation using lazy predict library remained unexplored. This study aims to create the LULC supervised classifier model for Sentinel 2 satellite images using lazy predict library and assess its capability for creating multiple machine learning models. The result of this study shows that lazy predict library can generate 26 machine learning models in efficient few lines of code and less time-consuming. Most LULC models generated by lazy predicts has performance metrics above 90% with time computation between 0 and 1 seconds. While lazy predict library has benefits to generate various machine learning models at once, it has drawbacks in terms of its feasibility for the machine learning production, its obstacle running in local environment, and its requirements for the RAM computation.
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