Paid crowdsourcing is a popular approach for creating training data in machine learning, but output quality can suffer from various drawbacks, such as noisy data. One solution is to obtain multiple acquisitions of the same dataset and perform integration steps, which can be challenging for geometries such as polygons. In this paper, we propose a raster-based polygon integration approach for the use of crowdsourced data, providing a solution for integrating multiple geometric shapes into single geometries. We analyze the effects of the choice of the integration threshold parameter for different sample sizes on the quality measures intersection over union (IoU) and Hausdorff distance, and provide a recommendation for its optimal selection based on empirical analysis. Additionally, further possibilities to improve integration results are explored, i.e., methods of filtering data before integration by outlier detection
