Temporal and spatial approaches for land cover classification

This paper describes solution for Time Series Land Cover Classification Challenge (TiSeLaC). Using features extracted from satellite images time series (SITS) each pixel corresponding to 30m⇥30m area can be classified to one of general class (urban area, forest, water, etc.). The following approaches implemented and evaluated: classical data mining multiclass prediction, local context embedding and extracting shapes of temporal dynamics. Also the di↵erent cross-validation schemes considered to evaluate performance of approaches.