Evaluation of Light Gradient Boosted Machine Learning Technique in Large Scale Land Use and Land Cover Classification

• Published in: Environments (Basel, Switzerland) Vol. 7; no. 10; p. 84
• Main Authors: McCarty, Dakota Aaron, Kim, Hyun Woo, Lee, Hye Kyung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.10.2020
• Subjects: Accuracy; Algorithms; Classification; Evaluation; Image classification; Land cover; Land use; land use and land cover maps; Learning algorithms; Machine learning; Model accuracy; Model testing; Natural resources; Neural networks; Remote sensing; Researchers; Satellite imagery; satellites; Support vector machines
• ISSN: 2076-3298; 2076-3298
• DOI: 10.3390/environments7100084

The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural resources. Algorithms for supervised classification of satellite images constitute a necessary tool for the building of these maps and they have made it possible to establish remote sensing as the most reliable means of map generation. In this paper, we compare three machine learning techniques: Random Forest, Support Vector Machines, and Light Gradient Boosted Machine, using a 70/30 training/testing evaluation model. Our research evaluates the accuracy of Light Gradient Boosted Machine models against the more classic and trusted Random Forest and Support Vector Machines when it comes to classifying land use and land cover over large geographic areas. We found that the Light Gradient Booted model is marginally more accurate with a 0.01 and 0.059 increase in the overall accuracy compared to Support Vector and Random Forests, respectively, but also performed around 25% quicker on average.