2D Shape Dataset of Proximity-Based Spatial Relation Concepts (GeoConShapes)

This dataset contains synthetic 2D images that depict basic spatial relation concepts such as ‘alone’, ‘close’, ‘far’, and ‘overlap’. The images include simple geometric shapes, artificial cracks and voids, and semantically segmented real-world objects (e.g., cats, birds, plants). Each image is labeled based on the spatial arrangement of the objects it contains. The data was generated using a Python script (available here: https://github.com/randoba/geoconshapes), which allows for the addition of new object types and even further spatial configurations with some coding effort. The dataset is intended for training and evaluating machine learning models to understand basic proximity relationships. The folder structure and naming convention reflect the spatial relation and object types in each image. This dataset is a result of Project C2279767 that has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2023 funding scheme.本数据集包含合成二维图像，用以呈现「单独存在」「邻近」「远离」「重叠」等基础空间关系概念。图像内容涵盖简单几何图形、人工裂纹与孔洞，以及经语义分割（semantically segmented）的真实世界物体（如猫、鸟类、植物）。每张图像均根据其包含物体的空间排布完成标注。 本数据集通过Python脚本生成（脚本开源地址：https://github.com/randoba/geoconshapes），用户仅需少量编码工作即可新增物体类型，乃至拓展更多空间关系配置。本数据集旨在用于训练与评估理解基础邻近关系的机器学习模型。 数据集的文件夹结构与命名规范，可对应每张图像的空间关系与物体类型。 本数据集源自C2279767号项目，该项目获得匈牙利文化与创新部下属国家研究、发展与创新基金支持，资助方案为KDP-2023。