A Connected Building Landscape dataset for Instance Segmentation

To address the scarcity of high-quality instance segmentation data for the unique architecture of high-density East Asian cities, this paper introduces the Connected Building Landscape dataset, a vital resource for urban planning, architectural analysis, and computer vision tasks. Researchers can utilize this dataset to train and benchmark a wide range of segmentation models, enabling a deeper understanding of East Asian architectural morphology. This detailed analysis provides urban planners with more precise tools and facilitates the generation of the hazard distribution maps or cultural heritage preservation priority reports. The dataset contains 2,801 JPEG images (1024 × 768 pixels) with polygonal segmentation masks, capturing diverse architectural styles and complex spatial layouts along National Route 1 from Tokyo to Osaka. All annotations are provided in a standard JSON format for easy model integration. Baseline experiments on models like Mask R-CNN and Mask2Former validate the dataset's quality and robustness for its intended tasks.