The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, and long-term management of the dating industry. Given the increasing availability of very-high spatial resolution (VHSR) images with limited spectral information, the high intra-class variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate large-scale mapping of date palm trees from multiscale and multidate VHSR images can be challenging. This webinar aims to shed light on these challenges and provide state-of-the-art solutions using advanced deep learning techniques. Specifically, attendees will gain a comprehensive understanding of how state-of-the-art semantic and instance segmentation methods can substantially enhance the accuracy and reliability of mapping from multiscale remote sensing datasets.