Aerial Building Classification Dataset (ABCD)

The dataset, entitled Aerial Building Classification Dataset (ABCD), was developed to support research on the classification of building types in high-resolution remote sensing imagery using deep learning methods. It consists of RGB aerial images acquired from an airborne platform, characterized by a spatial resolution of 0.25 m Ground Sampling Distance (GSD). The source data were derived from orthophotomaps provided by Geoportal.gov.pl, ensuring high geometric and radiometric quality. All images were standardized in terms of format and input size to ensure compatibility with convolutional neural network architectures. The ABCD dataset includes six classes of building objects: single-family buildings, multi-family buildings, agricultural (farm) buildings, industrial and commercial buildings, public utility buildings, and greenhouses. In total, the dataset comprises 26,379 image patches, divided into training, validation, and test subsets to support reproducible machine learning experiments. The class distribution is highly imbalanced, with single-family buildings constituting the majority class (67.84%), while minority classes such as public buildings (1.25%) and greenhouses (1.01%) are significantly underrepresented. This imbalance reflects real-world spatial distributions of building types but introduces additional challenges for model training, including the risk of bias toward dominant classes. An additional challenge inherent to the dataset is the high visual similarity between certain classes, particularly between residential, agricultural, and industrial buildings, which increases the difficulty of the classification task. As a result, the dataset provides a demanding benchmark for advanced classification models and enables the assessment of their robustness under realistic conditions. Overall, the ABCD dataset constitutes a high-quality resource for the development and evaluation of deep learning approaches in aerial image classification, with particular relevance to class imbalance handling, fine-grained visual discrimination, and real-world geospatial applications.本数据集命名为航空建筑分类数据集（Aerial Building Classification Dataset, ABCD），旨在支撑基于深度学习方法开展高分辨率遥感影像建筑类型分类的相关研究。该数据集包含由航空平台采集的RGB航空影像，其空间分辨率为0.25米地面采样距离（Ground Sampling Distance, GSD）。源数据取自Geoportal.gov.pl提供的正射影像图（orthophotomaps），具备优异的几何与辐射质量。所有影像均在格式与输入尺寸层面完成标准化处理，以适配卷积神经网络（Convolutional Neural Network）架构的兼容性要求。 ABCD数据集涵盖六类建筑对象：独栋住宅建筑、多户住宅建筑、农业（农场）建筑、工商建筑、公共事业建筑以及温室。数据集总计包含26379张图像切片，并划分为训练集、验证集与测试子集，以支持可复现的机器学习实验。该数据集的类别分布存在严重不平衡问题，其中独栋住宅建筑占比最高（67.84%），而公共建筑（1.25%）与温室（1.01%）等少数类别的占比显著偏低。 这种类别不平衡现象映射了现实世界中建筑类型的空间分布特征，但同时也为模型训练带来了额外挑战，包括偏向占优类别的偏差风险。本数据集固有的另一项挑战在于部分类别间视觉相似度极高，尤其是住宅、农业与工业建筑之间，这进一步提升了分类任务的难度。因此，该数据集可为先进分类模型提供极具挑战性的基准测试任务，并能够评估模型在现实场景下的鲁棒性。总体而言，ABCD数据集是开展航空影像分类深度学习方法开发与评估的高质量资源，尤其适用于类别不平衡处理、细粒度视觉判别以及现实地理空间应用相关研究。