As appointed in the EU Circular Economy Action Plan, cities and regions in EU member countries start accompanying their circular economy strategies by monitoring frameworks, often called Circular Economy Monitors (CEM). Having the task to assess the performance towards the achievement of set targets and to steer decision-making, CEMs need to rely on a multitude of statistics and datasets. Waste statistics play an important role in circular economy monitoring as they provide insights into the remaining linear part of the economy. The collection of waste statistics is mandated by the European Commission which provides general guidelines on data collection and processing. The Netherlands has one of the most detailed waste registries among the EU countries. The country's largest metropolitan region, Amsterdam, is currently building a CEM which tracks progress over time towards the set goals, highlights which areas need improvement and estimates target feasibility. This paper uses the Amsterdam CEM as a case-study to explore how the existing system of waste registration in the Netherlands is able to support decision-making. The data is explored with the help of four queries that relate to the CEM's goals and require data mapping to be answered. The data mapping and analysis process has revealed several limitations present in the waste data collection and a number of gaps present in current circular economy research and data analysis. At the same time, the available data already supports significant insights into the status quo of the current waste system and provides opportunities for circular economy monitoring.

We introduce a novel approach for the polygonization of Multi-view Stereo (MVS) meshes of buildings, which results in compact and topologically valid models. The main characteristic of our method is structure awareness, i.e., the recovery and preservation of the initial mesh primitives and their adjacencies. Our proposed methodology consists of three main stages: (a) primitive detection via mesh segmentation, (b) encoding of primitive adjacencies into a graph, and (c) polygonization. Polygonization is based on the approximation of the original mesh with a candidate set of planar polygonal faces. On this candidate set, we apply a binary labelling formulation to select and assemble an optimal set of faces under hard constraints that ensure that the final model is both manifold and watertight. Experiments on various building models demonstrate that our simplification method can produce simpler representations for both closed and open building meshes. Furthermore, these representations highly conform to the initial structure and are ready to be used for spatial analysis. The source code of this work is freely available at.