The Massachusetts dataset, created using vector data from the OpenStreetMap (OSM) platform, was observed to contain various types of labeling errors. Since the OSM data are continuously updated by volunteer contributors, manual data entry may bring the risk of inconsistency and inaccuracy [20]. Also, the resolution of the images exacerbates labeling errors by contributing to problems such as blurred building boundaries [21].
The Aerial Image Segmentation Dataset (AISD) is a high-resolution semantic segmentation dataset designed specifically for extracting buildings and roads. The original image is sourced from the online remote sensing images provided by OpenStreetMap and manually annotated. In our experiment, we selected building data from the Potsdam and Tokyo regions. The original image size for Potsdam was 3296 × 3296 pixels, while for Tokyo it was 2500 × 2500 pixels. In this dataset, we cropped the target area image into a 512 × 512 size image.
