Urbanscape-Net: A Spatial and Self-Attention Guided Deep Neural Network with Multi Scale Feature Extraction for Urban Land-Use Classification

In the present paper, a new deep neural network model, named, UrbanScape-Net, has been designed for improved land-cover classification. Motivated by the need to emphasize important features and capture intricate relationships in land-use scenes, spatial and self-attention mechanisms with the Xception architecture are integrated. The spatial attention module focuses on salient regions, the self-attention method captures intricate relationships within these regions and the dynamic convolutional layer with varying filter sizes extracts multi scale features. The Xception model, pretrained on ImageNet, has been employed as the foundational feature extractor. The experiment conducted on the UC Merced Land Use dataset demonstrated enhanced accuracy (of 98.83%) across 21 classes compared to state-of-the-art methods.