Recent advancements in deep learning and computer vision have led to widespread use of deep neural networks to extract building footprints from remote-sensing imagery. The success of such methods relies on the availability of large databases of high-resolution remote sensing images with high-quality annotations. The CrowdAI Mapping Challenge Dataset is one of these datasets that has been used extensively in recent years to train deep neural networks. This dataset consists of $ \sim\ $280k training images and $ \sim\ $60k testing images, with polygonal building annotations for all images. However, issues such as low-quality and incorrect annotations, extensive duplication of image samples, and data leakage significantly reduce the utility of deep neural networks trained on the dataset. Therefore, it is an imperative pre-condition to adopt a data validation pipeline that evaluates the quality of the dataset prior to its use. To this end, we propose a drop-in pipeline that employs perceptual hashing techniques for efficient de-duplication of the dataset and identification of instances of data leakage between training and testing splits. In our experiments, we demonstrate that nearly 250k($ \sim\ $90%) images in the training split were identical. Moreover, our analysis on the validation split demonstrates that roughly 56k of the 60k images also appear in the training split, resulting in a data leakage of 93%. The source code used for the analysis and de-duplication of the CrowdAI Mapping Challenge dataset is publicly available at https://github.com/yeshwanth95/CrowdAI_Hash_and_search .

翻译：近期深度学习和计算机视觉的进展推动深度神经网络广泛应用于从遥感影像中提取建筑足迹。此类方法的成功依赖于大规模高分辨率遥感图像数据库及其高质量标注的可用性。CrowdAI Mapping Challenge数据集是近年来常用于训练深度神经网络的此类数据集之一。该数据集包含约28万张训练图像和约6万张测试图像，所有图像均附带多边形建筑标注。然而，低质量与错误标注、图像样本大量重复以及数据泄漏等问题严重降低了基于该数据集训练的深度神经网络的实用性。因此，在使用该数据集前必须采用数据验证流程来评估其质量。为此，我们提出一个即插即用的流程，利用感知哈希技术实现对数据集的高效去重，并识别训练集与测试集之间的数据泄漏实例。实验表明，训练集中近25万张（约90%）图像存在重复。此外，对验证集的分析显示，约6万张图像中有5.6万张也出现在训练集中，导致93%的数据泄漏。用于CrowdAI Mapping Challenge数据集分析与去重的源代码已公开于 https://github.com/yeshwanth95/CrowdAI_Hash_and_search 。