Smart Landscapes: ML for Accurate Land Classification


Land-type classification is an essential aspect of resource planning, and correctly classifying a land type can play a crucial role in designing and executing efficient utilization of land types for agriculture and other purposes. In recent years, machine learning (ML) methods have become popular, shown significant improvements in their performance and have been applied in various domains. In this work, we have proposed an ML-based approach for efficient and accurate classification of land types. We extensively experimented with different ML methods such as decision tree (DT), support vector machine (SVM), random forests (RF) and K-nearest neighbour (KNN). The empirical results suggest that ML-based approaches are superior for land-type classification. It is also found that out of the different ML methods applied on Statlog Landsat dataset, SVM outperforms other methods and achieves 92% accuracy better than other state-of-the-art methods.

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Publication: Innovations in Electrical and Electronic Engineering

Publisher: Springer Link

Authors: Tejalal Choudhary, Arvind Kumar

Keywords: Land classification, Statlog, Landsat, Soil classification, GIS

Meet one of the Author:

Tejalal Choudhary

Dr Tejalal Choudhary received his Ph.D. in Computer Science Engineering from Bennett University in Greater Noida, India. He has expertise in computer vision, machine learning, deep learning, and model compression. His exceptional ability to think creatively, tackle problems from multiple angles, and generate innovative solutions sets him apart from others.


Bennett University, Greater Noida, India – Bennett University was established in the year 2016 by the Times Group which is India’s largest media conglomerate, to provide Ivy League quality education to undergraduate and postgraduate students. The six schools with 30+ programs and 70+ leading specialisations in Engineering, Management, Media, Law and Liberal Arts have positioned it as one of the top universities in India.

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