LULC-NEAT: Land Use Land Cover Classification Using NeuroEvolution of Augmenting Topologies
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 2
Abstract
Introduction/Importance of Study: NEAT's potency in optimizing neural networks for accurate LULC classification, aimed at better environmental stewardship, is shown. Novelty statement: LULC-NEAT introduces NeuroEvolution of Augmenting Topologies for optimizing neural networks in land use land cover classification. Material and Method: The EuroSAT RGB benchmark satellite dataset was preprocessed and evaluated using NEAT to create diverse feed-forward neural networks (FFNNs) with varying hidden layers. Result and Discussion: The NEAT-evolved FFNN architecture with two hidden layers showed excellent and high accuracy percentages during the training and testing, respectively. Although high training accuracy implies successful feature learning, it also indicates probable overfitting. However, the high accuracy obtained in testing, 99.83%, shows the excellent generalization ability of the model toward unseen data and thus does not overfit. The results were cross-validated with the state-of-the-art CNN models, and the experiments prove that NEAT can be effectively used for LULC classification. Concluding Remarks: The study supports that NEAT can effectively evolve neural networks for high-accuracy LULC classification, providing a robust alternative to traditional CNN models.
Authors and Affiliations
Sumayyea Salahuddin, Nasru Minallah, Muhammad AtharJavedSethi, Muhammad Ajmal, Maryam Mahsal Khan
Keywords
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