Context
Recklinghausen, located in North Rhine-Westphalia, together with the Westphalian University of Applied Sciences, has developed software based on artificial intelligence (AI), which automatically derives a map of impervious surfaces from aerial images and aggregates it to any area divisions. According to statistics from the German Federal Environmental Agency, currently about 45 per cent of settlements and traffic areas in Germany are ‘sealed’ i.e. air- and watertight, and thus not available for the infiltration of rainwater. In addition, sealed surfaces are usually potential heat stores, which contribute greatly to the warming of urban areas. In view of the increasing threat of climate change, precise and continuous monitoring of sealed surfaces is an important urban planning tool for effective drainage control and avoidance of heat islands.
In the past, explicit mappings of impervious surfaces were generally recorded manually on the basis of image evaluations, but such manual mapping and its maintenance is time-consuming and costly. Given the precise and continuous acquisition of the properties via image, laser and other sensors, it is much more effective to automatically detect sealing surfaces using AI-based systems. The software generates object-sharp maps based on overflight image data.
Objectives
The aim of the automatic detection of impervious surfaces software is to enable the assessment of areas more precisely and in more detail with regard to their climate-ecological nature. In turn, these findings can serve as the basis for action concepts and adaptation measures to implement climate-effective measures for the district cities in the next step. The software provides important support in the targeted planning and evaluation of measures for more climate and environmental protection in cities, and in the implementation of sustainable, municipal land management.
Implementation
The software for automated detection and classification of impervious surfaces was developed by the district of Recklinghausen and carried out jointly with the Westphalian University of Applied Sciences. The project was carried out over 12 months within the framework of funding for a research associate at the University of Westphalia in the Department of Electrical Engineering and Applied Natural Sciences of just under €100 000. The University of Westphalia was a key part of the project, due to its expertise in the field of deep learning and computer vision in relation to geodata. In regular project meetings between the University of Westphalia and the cities involved, the objectives were defined and the progress of the project was continuously monitored. In the case of the district of Recklinghausen, employees from the areas of land registry and geo-information, as well as environment and climate protection, were involved.