1. Crowdsourced Mapping: Crowdsourced data from OpenStreetMap is used to automate the classification of satellite images into different land cover classes (impervious, farm, forest, grass, orchard, water).
2. El Nino: The data set contains oceanographic and surface meteorological readings taken from a series of buoys positioned throughout the equatorial Pacific.
3. Robot Execution Failures: This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals
4. Steel Plates Faults: A dataset of steel plates’ faults, classified into 7 different types.
The goal was to train machine learning for automatic pattern recognition.