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

Efficient Deduplication and Leakage Detection in Large Scale Image Datasets with a focus on the CrowdAI Mapping Challenge Dataset