no code implementations • 27 Sep 2021 • Jeremy Diaz, Guido Cervone, Christelle Wauthier
Here, we explore forecasting this thermal data stream from a deep learning perspective using existing architectures that model sequences with varying spatiotemporal considerations.
no code implementations • 9 Jul 2021 • Fangcao Xu, Jian Sun, Guido Cervone, Mark Salvador
Atmospheric correction errors can significantly alter the spectral signature of the observations, and lead to invalid classifications or target detection.
no code implementations • 8 Mar 2021 • Weiming Hu, Guido Cervone, George Young, Luca Delle Monache
The central core of the AnEn technique is a similarity metric that sorts historical forecasts with respect to a new target prediction.
no code implementations • 4 Feb 2021 • Manzhu Yu, Fangcao Xu, Weiming Hu, Jian Sun, Guido Cervone
Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.
no code implementations • 26 Sep 2019 • Alessandro Fanfarillo, Behrooz Roozitalab, Weiming Hu, Guido Cervone
In order to provide a meaningful probabilistic forecast, the AnEn method requires storing a historical set of past predictions and observations in memory for a period of at least several months and spanning the seasons relevant for the prediction of interest.