Machine Learning on Geospatial Data with Malte Loller-Anderson & Mathilde Ørstavik
Aug 29, 2024
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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.
Machine learning significantly enhances the automation of identifying buildings in aerial imagery, improving mapping efficiency and accuracy.
Norkart's decision to utilize in-house nVidia L40 processors for training models reflects a commitment to optimizing data processing capabilities.
The discussion highlights the challenges of differentiating between actual buildings and similar natural features, underscoring the complexity of geospatial data analysis.
Deep dives
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Updates on NDC Conferences
The hosts, Karl and Richard, provide updates for the upcoming NDC conferences scheduled for 2024, including the Copenhagen Developers Festival in August and NDC Porto in October. Attendees are encouraged to purchase tickets early to benefit from discounted rates and to participate in this prominent developer-focused event. These conferences are anticipated to foster community engagement among DotNet developers, providing learning and networking opportunities. By highlighting these events, the hosts emphasize the value of participation within the developer community.
Automotive Technology Insights
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Geospatial Data and Innovations
The podcast features guests who delve into the field of geospatial data and machine learning applications in Norway. By utilizing aerial imagery and advanced machine learning techniques, the team aims to automate the detection of buildings and enhance mapping accuracy. They discuss challenges such as managing data quality and the need for sophisticated algorithms to draw meaningful insights from vast datasets. This conversation showcases the transformative potential of technology in improving urban planning and environmental management.
What can machine learning do for geospatial data? Carl and Richard talk to Malte Loller-Anderson and Mathilde Ørstavik about their work at Norkart, using aerial imagery to build detailed maps around Norway. Mathilde dives into the critical role of machine learning - identifying buildings in images. Usually done by hand with each new image, Norkart has a machine learning model that automates the process trained on previous vector maps of buildings. But there are many things that look like buildings in Norway, including patches of snow, mountains, and even shapes under water. Malte also discusses how Norkart has decided to train in-house with nVidia L40 processors rather than in the cloud - the hardware is used 24 hours a day since some models can take weeks to train! There are many interesting ideas about geospatial data and machine learning from people who have been doing it for years.
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