Modern smartphones are equipped with built-in magnetometers that capture disturbances of the Earth's magnetic field induced by ferromagnetic objects. In indoor environment, using magnetic field data turns to be a strong alternative to conventional localization techniques as requiring no special infrastructure. We revise the state of the art methods based on landmark classification [5] and propose a novel approach. We represent magnetic data time series as image sequences and compose multi-channel input to a deep neural network. We use four methods, Recurrence plots, Gramian Angular Fields and Markov Transition Fields, to capture different patterns in magnetic data stream. We complete the landmark-based classification with deep regression on the user's position and combine convolutional and recurrent layers in the deep network. We evaluate our methods on the recently published MagPie dataset [3] and show that they outperform the state of the art methods.