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NYC‑Indoor‑VPR is a unique indoor visual place recognition dataset created by New York University, comprising over 36,000 images captured from 13 crowded scenes across New York City under varying lighting and appearance conditions. The dataset was built using a semi‑automatic labeling method to establish ground truth locations for each image. It is primarily intended for indoor localization and navigation research, especially for robots and assistive navigation systems, addressing the challenges of visual aliasing and occlusion in indoor environments.

These datasets were obtained in a real department store environment and contain hundreds of dynamic objects, such as humans. Please refer to the NAVER LABS website for detailed explanations.