AUTHOR=Chandnani Chirag Jitendra , Kulkarni Shlok Chetan , Amali D Geraldine Bessie , Selvaraj Rohini 

TITLE=A new framework with convoluted oscillatory neural network for efficient object-based land use and land cover classification on remote sensing images

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1696859

DOI=10.3389/frai.2025.1696859

ISSN=2624-8212

ABSTRACT=Rigorous urbanization leads to unprecedented climate change. Pune area in India has witnessed recent flash floods and landslides due to unplanned rapid urbanization. It, therefore, becomes vital to manage and analyse man-made impact on the environment through effective land use land cover classification (LULC). Accurate LULC classification allows for better planning and effective allocation of resources in urban development. Remote sensing images provide surface reflectance data that are used for accurate mapping and monitoring of land cover. Convolution neural networks (CNN) trained with Relu are conventionally used in classifying different land types. However, every neuron has a single hyperplane decision boundary which restricts the model's capability to generalize. Oscillatory activation functions with their periodic nature have demonstrated that a single neuron can have multiple hyperplanes in the decision boundary which helps in better generalization and accuracy. This study proposes a novel framework with convoluted oscillatory neural networks (CONN) that synergistically combines the periodic, non-monotonic nature of oscillatory activation functions with the deep convoluted architecture of CNNs to accurately map LULC. Results carried out on LANDSAT-8 surface reflectance images for the Pune area indicate that CONN with Decaying Sine Unit achieved an overall train accuracy of 99.999%, test accuracy of 95.979% and outperforms conventional CNN models in precision, recall and User's Accuracy. A thorough ablation study was conducted with various subsets of the feature set to test the performance of the selected models in the absence of data.