# Data Leakage Detection and De-duplication in Large Scale Geospatial Image Datasets

arXiv: https://arxiv.org/abs/2304.02296 · HTML: https://mlsanity.com/paper/2304.02296/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [A Self-Supervised Descriptor for Image Copy Detection](https://mlsanity.com/paper/2202.10261/) (2022)
- [Topological Map Extraction from Overhead Images](https://mlsanity.com/paper/1812.01497/) (2018)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [A Survey on Locality Sensitive Hashing Algorithms and their Applications](https://mlsanity.com/paper/2102.08942/) (2021)
- [Machine-learned Regularization and Polygonization of Building Segmentation Masks](https://mlsanity.com/paper/2007.12587/) (2020)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication](https://mlsanity.com/paper/2109.00899/) (2021)

## Built on by (most cited first, 4 of 4)

- [Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery](https://mlsanity.com/paper/2412.07899/) (2024)
- [Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing](https://mlsanity.com/paper/2505.01385/) (2025)
- [SegAssess: Panoramic quality mapping for robust and transferable unsupervised segmentation assessment](https://mlsanity.com/paper/2509.01183/) (2025)
- [BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe](https://mlsanity.com/paper/2606.20909/) (2026)

## Abstract (arXiv)

In our study, we conducted a comprehensive analysis of three widely used datasets in the domain of building footprint extraction using deep neural networks: the INRIA Aerial Image Labelling dataset, SpaceNet 2: Building Detection v2, and the AICrowd Mapping Challenge datasets. Our experiments revealed several issues in the AICrowd Mapping Challenge dataset, where nearly 90% (about 250k) of the training split images had identical copies, indicating a high level of duplicate data. Additionally, we found that approximately 56k of the 60k images in the validation split were also present in the training split, amounting to a 93% data leakage. Furthermore, we present a data validation pipeline to address these issues of duplication and data leakage, which hinder the performance of models trained on such datasets. Employing perceptual hashing techniques, this pipeline is designed for efficient de-duplication and leakage identification. It aims to thoroughly evaluate the quality of datasets before their use, thereby ensuring the reliability and robustness of the trained models. Our code is available at https://github.com/yeshwanth95/Hash_and_search .