The trading of dry bulk commodities, such as coal, grain, and iron ore, relies heavily on accurate data regarding stockpiles and shipments at ports.
Traditional methods of monitoring commodity levels at ports can be slow and prone to human error. Furthermore, market analysts struggle to obtain timely and reliable information to make informed trading decisions, which can lead to missed opportunities and increased risks.
By utilising EO data, Lyrasense allows users to integrate insights into their internal decision making process by monitoring dry bulk commodity levels at various ports. The workflows developed and deployed on Lyrasense employ advanced imaging and machine learning algorithms to analyse satellite imagery, allowing traders to monitor stockpile sizes, loading and unloading activities, and port congestion. This data empowers market analysts to assess supply levels, anticipate market shifts, and make informed decisions, thereby enhancing trading strategies and optimising investment outcomes.
