ARTIFICIAL INTELLIGENCE MODEL FOR DEMAND PREDICTION THROUGH REMOTE SENSING

Remote sensing is a rapidly growing research area that involves the collection and analysis of information about the Earth’s surface and natural and anthropic phenomena, using sensors on board satellites, aircraft or drones [Chen et al., 2020]. This technology has been applied in various fields, such as environmental monitoring, natural resource management, urban planning and demand forecasting in critical sectors, such as energy, agriculture and transportation [Wang et al., 2019]. In recent years, artificial intelligence (AI) has been increasingly integrated with remote sensing, allowing for more efficient analysis and interpretation and accuracy of the collected data [Zhang et al., 2018]. Machine learning and deep neural networks, in particular, have shown great potential to improve image classification, object detection and time series analysis in remote sensing data [Ma et al., 2019].