Proposed design of an augmented deep learning model for estimation of sustainable development goals via satellite image analysis

This research paper presents the design of an augmented deep learning model for the estimation of Sustainable Development Goals (SDGs) through the analysis of satellite images. The proposed model is a fusion of VGGNet, ResNet, and Inception Net architectures, which are commonly used for image classification tasks. To improve the model's accuracy and efficiency, we applied Grey Wolf Optimization for augmentation of image sets. The objective is to identify the critical features in satellite images that are indicative of sustainable development and can aid in monitoring SDG progress. This research is motivated by the pressing need to monitor and achieve SDGs, which are crucial for global development. Although existing models have been applied to estimate SDG progress, they often suffer from low accuracy and efficiency, limiting their potential applications. Our proposed model aims to overcome these limitations by using a fusion of deep learning models and a bioinspired optimization algorithm to improve the estimation of SDGs through satellite image analysis. The proposed model's performance is compared to other state-of-the-art models, demonstrating significant improvement in accuracy and efficiency. The results show that the proposed model can accurately estimate SDGs through satellite image analysis, which has significant implications for sustainable development monitoring and policy-making scenarios. Deep learning models have revolutionized various fields, including computer vision, natural language processing, and robotics. They have the potential to enhance our understanding of complex systems and facilitate decision-making processes. Bioinspired models, such as Grey Wolf Optimization, are increasingly gaining attention due to their ability to solve complex optimization tasks. This research paper highlights the potential of combining deep learning and bioinspired models for sustainable development monitoring, which can aid in achieving global development goals.