End-to-end learning of deep spatio-temporal representations for satellite image time series classification

In this paper we describe our first-place solution to the discovery challenge on time series land cover classification (TiSeLaC), organized in conjunction of ECML PKDD 2017. The challenge consists in predicting the Land Cover class of a set of pixels given their image time series data acquired by the satellites. We propose an end-to-end learning approach employing both temporal and spatial information and requiring very little data preprocessing and feature engineering. In this report we detail the architecture that ranked first-out of 21 teams-comprising modules using dense multi-layer perceptrons and one-dimensional convolutional neural networks. We discuss this architecture properties in detail as well as several possible enhancements.