Context _(From original page)_ Symphony Lake Dataset consists of 121 visual surveys of a lakeshore over more than three years in Metz, France. Unique from roadway datasets, it adds breadth to a space at a time when larger and more diverse datasets are needed to train data hungry machine learning methods. Over 5 million images from an unmanned surface vehicle capture the unstructured, natural environment as it evolved over time. Significant variation in appearance is present on time scales of weeks, seasons, and years. Success in this space may demonstrate advancements in perception, SLAM, and environment monitoring. Content This is just a portion of the dataset covering the downsampled images recorded from the vehicle. They are organized by directories representing the month and datestamps and images containing the different positions and angles captured. Acknowledgements The full details and links to other components are available from the following original site: http://dream.georgiatech-metz.fr/?q=node/79 # Please cite the following paper if you use the dataset: - Griffith, Shane; Chahine, Georges; Pradalier, Cédric; Symphony Lake Dataset, Submitted to IJRR, 2017. # Relevant Papers: - Griffith, Shane; Pradalier, Cédric; Reprojection Flow for Image Registration Across Seasons, British Machine Vision Conference (BMVC), 2016 - Griffith, Shane; Pradalier, Cédric; Survey Registration for Long-Term Natural Environment Monitoring, Journal of Field Robotics, 2016 Inspiration Some of the C++ code for processing the data can be found on github at https://github.com/cedricpradalier/SymphonyLakeDataset and details for procesing the CSV files in particular can be found at https://github.com/cedricpradalier/SymphonyLakeDataset/blob/master/include/symphony_lake_dataset/csv_functions.h