  Deep learning techniques are becoming increasingly important to solve a
number of image processing tasks. Among common algorithms, Convolutional Neural
Networks and Recurrent Neural Networks based systems achieve state of the art
results on satellite and aerial imagery in many applications. While these
approaches are subject to scientific interest, there is currently no
operational and generic implementation available at user-level for the remote
sensing community. In this paper, we presents a framework enabling the use of
deep learning techniques with remote sensing images and geospatial data. Our
solution takes roots in two extensively used open-source libraries, the remote
sensing image processing library Orfeo ToolBox, and the high performance
numerical computation library TensorFlow. It can apply deep nets without
restriction on images size and is computationally efficient, regardless
hardware configuration.
