A modified vision transformer framework for image-based land cover segmentation in rural architectural design and planning

Sustainable development, cultural preservation, and rural quality of life depend on rural architectural design and planning. It balances modern infrastructure needs with rural ecological and social characteristics to ensure sustainable growth. This research presents a deep learning framework based on a modified and robust vision transformer optimized with a modified firefly algorithm to perform image-based land cover segmentation in rural architectural design and planning. The proposed model formulates a high-dimensional feature space from fixed-size patches, then performs positional encodings and transformer encoder layers to maintain spatial information within these patches. Then, it embeds the self-attention mechanism of these layers to capture the interdependence of global spatial regions, which is crucial for bifurcating the region of interest. The optimization of the features and selection of hyper-parameters are performed with a modified firefly algorithm. The framework is validated using the EuroSAT benchmark dataset, derived from Sentinel-2 satellite imagery, consisting of 27,000 geo-referenced samples across 10 balanced land cover classes such as annual crop, forest, herbaceous vegetation, highway, and residential areas. Each image has a spatial resolution of 64 × 64 pixels in RGB format, ensuring consistent representation of rural and urban features. The quality of the segmented regions achieved an overall accuracy of 99.5% with a kappa coefficient of 0.81, while the mean fitness value was 4.9930 × 10^-8 on the publicly available rural dataset. The results are compared with state-of-the-art, pre-trained existing models like VGG-19, ResNet-50, DarkNet-19, Inception-V3, and other reported techniques like Gaussian Naïve Bayes, geosystem approach, deep CNN and fuzzy DL hybrid with chaotic PSO that have the classification accuracy of 89.96%, 91.52%, 86% and 93.3%, respectively. The stability of the proposed model is investigated through Monte Carlo simulation based on 200 independent runs and their statistical investigations. The proposed predicted system is quite helpful for rural areas' transformation into urbanization by mitigating climate change, natural resources and geographical characteristics.