African Vulture Optimization with Deep Learning based Geographical Information Analytics for Land Cover Segmentation and Classification - Anil Antony (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Owing to the complications of LULC, the numerous kinds of seasonal variations, and human actions, Land use and land cover (LULC) become a challenge for monitoring and identification. Therefore, machine learning (ML) and Remote sensing (RS) technologies are employed to overcome these issues for generating LULC maps. RS identification uses object-based and pixel-based classifications which deliver LU classification with higher performance. However, this technique constantly needs sample data for parameter adjustment and training. ML has been instrumental in RS classification and has achieved remarkable outcomes for the LULC classification. Furthermore, currently, semantic segmentation is generally utilized in remote sensing images (RSI) for mapping crop types, glacial lakes, LC, and buildings. In the present scenario, Convolution Neural Networks (CNN) have attained more effective results for numerous tasks comprising LC estimates due to their capability to remove multiscale feature maps. The most complex issue in standard spatial resolution images is employing deep learning (DL) semantic segmentation for LU removal. Therefore, this study presents an optimum DL-based segmentation and classification method for LULC, termed the ODLSC-LULC approach. The segmentation stage uses U2Net, a strong DL-based segmentation system, to exactly describe spatial features and improve contextual understanding. The SE-ResNet architecture is employed for feature extraction, taking hierarchical representations of land features for more discriminative identification. To modify the model's parameters efficiently, we present the African Vulture Optimizer Algorithm (AVOA), which represents the foraging behavior of vultures to constantly enhance the network's configuration. At last, a Bidirectional Long Short-Term Memory (BiLSTM) classifier is utilized to analyze successive dependencies and safeguard the precise classification of different land cover classes. Experimental outcomes on benchmark datasets determine the better performance of ODLSC-LULC, showcasing its efficiency in attaining optimum classification and segmentation outcomes for difficult and dynamic LU scenarios.