LULC-NEAT: Land Use Land Cover Classification Using NeuroEvolution of Augmenting Topologies

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 introducesNeuroEvolution 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 neuralnetworks (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 supportsthat NEAT can effectively evolve neural networks for high-accuracy LULC classification, providinga robust alternative to traditional CNN models.