Alessio Barbaro Chisari, Alessandro Ortis, Luca Guarnera, Wladimiro Carlo Patatu, Rosaria Ausilia Giandolfo, Emanuele Spampinato, Sebastiano Battiato, Mario Valerio Giuffrida
ICIP (2024)
Casella, B., Chisari, A. B., Aldinucci, M., Battiato, S., & Giuffrida, M. V. (2024). Federated Learning in a Semi-Supervised Environment for Earth Observation Data. In ESANN 2024 Proceedings-32th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 93-98).
Abstract
Accurate weather monitoring depends significantly on cloud detection, a crucial process achievable through remote sensing tools such as satellite imagery and radar or through the analysis of data obtained from ceilometers. A ceilometer is a lidar-based device allowing to analyse the atmosphere and detect the presence of particles within clouds. The data retrieved from ceilometers involve analysis of the backscatter of the lidar signal returning to the surface. Given the inherent noise in this data, we leverage deep learning models to detect the presence of clouds in the data. To label the data, we take advantage of a Weather Research & Forecasting (WRF) model, which provided us with ground-truth used for validation purposes. We performed a comparative analysis with current state-of-the-art deep learning architectures on this specialist domain. This comparative analysis shows that the best model is ResNet 50, but also a transformer-based model, such as ViT, achieves great results. These preliminary results pave the scenario for future works aimed at detecting other particles composing the atmosphere, such as polluting agents that can be detected from the ceilometer backscatter data.