Fischer, RaphaelRaphaelFischerPiatkowski, NicoNicoPiatkowskiPelletier, CharlotteCharlottePelletierWebb, Geoffrey I.Geoffrey I.WebbPetitjean, FrançoisFrançoisPetitjeanMorik, KatharinaKatharinaMorik2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/41025710.1109/DSAA49011.2020.00069Spatio-temporal data sets such as satellite image series are of utmost importance for understanding global developments like climate change or urbanization. However, incompleteness of data can greatly impact usability and knowledge discovery. In fact, there are many cases where not a single data point in the set is fully observed. For filling gaps, we introduce a novel approach that utilizes Markov random fields(MRFs). We extend the probabilistic framework to also consider empirical prior information, which allows to train even on highly incomplete data. Moreover, we devise a way to make discrete MRFs predict continuous values via state superposition. Experiments on real-world remote sensing imagery suffering from cloud cover show that the proposed approach outperforms state-of-the-art gap filling techniques.enprobabilistic machine learninggap fillingspatio-temporalgraphical modelremote sensing005006629conference paper