Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mahendra, H.N.; Pushpalatha, V.; Rekha, V.; Sharmila, N.; Kumar, D. Mahesh; Pavithra, G.S.; Basavaraj, N.M.; Mallikarjunaswamy, S. Title A name given to the resource Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Nature Environment and Pollution Technology;Volume;24;Issue;2;Article No.;B4238; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.46488/NEPT.2025.v24i02.B4238" target="_blank" rel="noreferrer noopener">https://doi.org/10.46488/NEPT.2025.v24i02.B4238</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007724406?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007724406?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mahendra H.N., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Pushpalatha V., Department of Information Science and Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Rekha V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bangalore, 560060, India; Sharmila N., Department of Electrical and Electronics Engineering, JSS Science and Technology University, Karnataka, Mysuru, 570015, India; Kumar D.M., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Pavithra G.S., Department of Computer Science and Engineering, AI-ML, RNS Institute of Technology, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560098, India; Basavaraj N.M., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India; Mallikarjunaswamy S., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, Bengaluru, 560060, India Description An account of the resource Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data. 2025 by the authors. Subject The topic of the resource Land use/land cover; Linear Imaging Self-Scanning Sensor-III; Machine learning; Multispectral data; Random forest classifier; Remote sensing Publisher An entity responsible for making the resource available Technoscience Publications Relation A related resource ISSN: 9726268; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online