Comparison of Various Deep CNN Models for Land Use and Land Cover Classification

• Published in: Intelligent Human Computer Interaction Vol. 13184; pp. 499 - 510
• Main Authors: Mahamunkar, Geetanjali S., Netak, Laxman D.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Deep learning; Geospatial data analysis; Human centric deep learning API; Land Use and Land Cover Classification; Pre-trained deep CNN models
• ISBN: 9783030984038; 3030984036
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-98404-5_46

Activities of identifying kinds of physical objects on lands from the images captured through satellite and labeling them according to their usages are referred to as Land Use and Land Cover Classification (LULC). Researchers have developed various machine learning techniques for this purpose. The effectiveness of these techniques has been individually evaluated. However, their performance needs to be compared against each other primarily when they are used for LULC. This paper compares the performance of five commonly used machine learning techniques, namely Random Forest, two variants of Residual Networks, and two variants of Visual Geometry Group Models. The performance of these techniques is compared in terms of accuracy, recall and precision using the Eurosat dataset. The performance profiling described in this paper could help researchers to select a given model over other related techniques.