These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover.

This is a collection of paired thermal and visible ear images. Images in this dataset were acquired in different illumination conditions ranging between 2 and 10700 lux. There are total 2200 images of which 1100 are thermal images while the other 1100 are their corresponding visible images. Images consisted of left and right ear images of 55 subjects. Images were capture in 5 illumination conditiond for every subjects. This dataset was developed for illumination invariant ear recognition study. In addition it can also be useful for thermal and visible image fusion research.

Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.