Multimodal Deep Learning: Combining Road Imagery and Weather Data to Predict Wind Farm Access and Energy Operations

Abstract

Access to wind farm sites in Nigeria has remained a persistent challenge due to the combined effects of poor road infrastructure and adverse weather conditions. These factors have limited the efficiency of logistics and maintenance operations, consequently affecting the sustainability of wind energy projects. This study developed a multimodal deep learning framework that integrated road surface imagery and meteorological data to predict road accessibility to wind farm locations across Nigeria. Road surface data were obtained from the Humanitarian Data Exchange (HeiGIT, 2024), while meteorological variables, including rainfall, temperature, and humidity, were retrieved from NASA’s POWER API. The integrated model, combining convolutional and recurrent neural network layers, achieved an overall accuracy of 92.4% and an F1-score of 0.89, outperforming unimodal baselines. Results revealed that rainfall and humidity exerted the most significant influence on road navigability, reducing accessibility scores by up to 40% in high-precipitation regions. The findings demonstrated the potential of multimodal AI to enhance predictive infrastructure management and support sustainable wind farm operations in developing contexts such as Nigeria.