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Develop your algorithms with diverse data from all over the world, including different weather, season, time of day, camera, and viewpoint conditions. A benchmark for visual localization in outdoor scenes.
Mapillary Street-level Sequences Dataset . Contribute to mapillary/mapillary_sls development by creating an account on GitHub.
A diverse street-level imagery dataset with bounding box annotations for detecting and classifying traffic signs around the world. This Mapillary Traffic Sign Dataset is provided under the Creative Commons Attribution NonCommercial Share Alike (CC BY-NC-SA) license.
A diverse street-level imagery dataset with pixel‑accurate and instance‑specific human annotations for understanding street scenes around the world. This Mapillary Vistas Dataset is provided under the Creative Commons Attribution NonCommercial Share Alike (CC BY-NC-SA) license.
The dataset is built upon images extracted from www.mapillary.com, a community-led platform for visualizing the world through street-level photos. Anyone can contribute photos of various places, and the data is available under a CC-BY-SA license agreement.
We have introduced the Mapillary Vistas Dataset - a novel, large-scale dataset of street-level images for the tasks of semantic segmentation and instance-specific semantic segmentation.
Mapillary Metropolis is a city-scale, multi-modality benchmarking dataset for researching and evaluating computer vision algorithms in the context of 3D semantic map making.
The largest and most diverse dataset for lifelong place recognition from image sequences in urban and suburban settings. This Mapillary Street-level Sequences Dataset is provided under the Creative Commons Attribution NonCommercial Share Alike (CC BY-NC-SA) license.
Mapillary is the platform that makes street-level images and map data available to scale and automate mapping. We’re committed to building a global service for everyone.
The dataset contains pairs of image sequences with ground truth poses obtained through human annotation of control points, depicting urban and suburban scenes with challenging appearance and viewpoint changes.