Speaker
Ricardo Zanmar Sanchez
(Istituto Nazionale di Astrofisica (INAF))
Description
Continuous monitoring of environmental conditions at astronomical observatories is critical for optimizing operations and assessing the integrity of observational data. All-sky cameras are a cost-effective way to capture a 180° field of view to monitor clouds and other adverse weather. In this work, we apply several supervised ML algorithms to classify all-sky images by atmospheric condition. Furthermore, for images with partial cloud cover, we estimate the coverage percentage using segmentation techniques. These results are used to classify thousands of archival images and have the potential to inform both real-time observatory operations and future data reduction.