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            <funder>L.P. was supported by a CIFRE doctoral fellowship from the ANRT and Stellantis (agreement #2019/0658). J.R.S has received funding from the Excellence Initiative of Aix-Marseille Université - A*Midex, a French ”Investissements d’Avenir” programme AMX-21-ERC-02 and AMX-20-TRA-043. This research work was also supported by the SUD Provence-Alpes-Côte d’Azur Region (PACA) (Grant #2021/08135).</funder>
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                <term xml:lang="en">Skylight Polarization</term>
                <term xml:lang="en">Navigation</term>
                <term xml:lang="en">Deep Learning</term>
                <term xml:lang="en">Heading</term>
                <term xml:lang="en">Celestial Compass</term>
                <term xml:lang="en">Polarized Vision</term>
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              <p>Skylight polarization phenomenon is at the origin of a recent growing interest for bio-inspired navigation. Skylight based orientation sensors can be simulated on the basis of physical models. In parallel, machine learning algorithms require a large amount of data to be trained. However, while some simulated databases already exists in the literature, a public database composed of real-world color polarimetric images of the sky in various weather conditions does not. In this study, a long-term experimental device is presented, designed to be left in a distant roof to acquire data over several months, using a Division-of-Focal-Plane polarization imager with a fisheye lens mounted on a rotative telescope mount. An open-source mechanical and electrical design is proposed for easy replication at other locations, with an algorithm to get the sensor's orientation and geometrical calibration in the East-North-Up frame. A sample one-month long dataset is provided on a public archive.</p>
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