High Resolution Mapping

High-resolution mapping aims to create detailed spatial representations of various phenomena, from subsea infrastructure to air pollution levels and geological features. Current research emphasizes the use of advanced machine learning techniques, including deep ensemble forests and deep learning-based instance segmentation models, to process high-resolution imagery and sensor data, often coupled with regularization techniques to handle data limitations. These advancements enable more accurate and efficient mapping across diverse applications, improving scientific understanding and informing decision-making in fields ranging from environmental monitoring to resource exploration and infrastructure management. The development of real-time processing capabilities further enhances the practical utility of these methods.