Malte Loller-Anderson from Norkart specializes in machine learning for geospatial data, while Mathilde Ørstavik focuses on automating the identification of buildings in aerial imagery. They discuss how their models streamline the mapping process by recognizing structures in challenging environments, such as snow and mountainous landscapes. Malte highlights their decision to use in-house nVidia L40 processors instead of cloud solutions, optimizing training times for complex models. Their insights illuminate the future of geospatial technology in urban planning and environmental monitoring.

Malte Loller-Anderson from Norkart and Mathilde Ørstavik, an expert in machine learning for identifying buildings, share fascinating insights into using AI for geospatial data. They discuss how Norkart automates building recognition from aerial imagery, tackling challenges like distinguishing between real structures and natural formations. They also explore the decision to use in-house nVidia processors for training models, emphasizing the intricacies of managing vast datasets and the potential benefits for urban planning and emergency services.