Detecting manmade structures from space

• Radboud University: Aafke Schipper, Konrad Mielke, Mark Huijbregts, Tom Claassen
• External partners: Gertjan Geerling (Deltares), Johan Meijer (PBL Netherlands Environmental Assessment Agency)

Manmade structures like roads, railways, mines, dams and (aquaculture) farms provide a multitude of essential societal benefits, including accessibility, transport, resources, energy generation, water security and food. However, they also have negative consequences for the environment. Applied environmental research institutes like Deltares and PBL Netherlands Environmental Assessment Agency need up-to-date georeferenced information on these artificial structures in order to evaluate the trade-offs among their benefits and impacts. Currently, however, this information is not comprehensively available across the globe. The ongoing developments in machine learning and remote sensing open up promising opportunities for alternative approaches to manual geo-referencing for mapping manmade structures. This project will prepare a grant proposal that focuses on developing and applying novel machine-learning approaches for the global mapping of manmade structures based on remote sensing imagery. In parallel, the applicants will provide a proof of concept by building and evaluating a prototype of a convolutional neural network (CNN) trained to detect dams from satellite imagery.