Camille works on machine learning applications at Esri R&D Center in Zürich. As a member of the Urban Planning team, her role includes providing innovative ideas where Machine and Deep Learning solutions can add value. Camille studied Microengineering at Ecole Polytechnique Fédérale de Lausanne (EPFL) and received a Master’s degree in Robotics and Autonomous Systems. As a student, she had a great interest in automation and technology in order to make people’s lives easier as well as implement environmentally-friendly tools. Her last academic project was about smart building solutions for improving energy consumption using Machine Learning. In addition to enabling machines to learn by themselves, Camille is a mountain lover.
Building Modeling Optimization Using Machine Learning
CityEngine and ArcGIS Urban are very efficient tools to plan, model and build thoughtful urban environments. They make the 3D visualization of cities fast and interactive. Using the procedural modeling approach, CityEngine and Urban allow the users to easily create and generate building models. The city models can be adjusted by varying their parameters. However, cities impose restrictive zoning rules on development projects. These regulatory constraints are complicated to fulfill and fine-tuning the building model parameters can be tedious. In order to improve the design process, we apply machine learning to automatically generate the most interesting building designs while complying with the city zoning rules.