This remote module of the [Orfeo ToolBox](https://www.orfeo-toolbox.org)(OTB) aims to provide a deep learning framework targeting remote sensing images processing.
This remote module of the [Orfeo ToolBox](https://www.orfeo-toolbox.org)provides a generic, multi purpose deep learning framework, targeting remote sensing images processing.
It contains a set of new process objects that internally invoke [Tensorflow](https://www.tensorflow.org/), and a bunch of user-oriented applications to perform deep learning with real-world remote sensing images.
Applications can be used to build OTB pipelines from Python or C++ APIs.
*Main highlights*
- Sampling,
- Training, supporting save/restore/import operations (a model can be trained from scratch or fine-tuned),
- Serving models with support of OTB streaming mechanism
- Serving models with support of OTB streaming mechanism. Meaning (1) not limited by images sizes, (2) can be used as a "lego" in any OTB pipeline and preserve streaming, (3) MPI support available (use multiple processing unit to generate one single output image)
*Portfolio*
Below are some screen captures of deep learning applications performed at large scale with OTBTF.
- Image to image translation (Spot-7 image --> Wikimedia Map using CGAN)
<imgsrc ="doc/pix2pix.png"/>
- Landcover mapping (Spot-7 images --> Building map using semantic segmentation)
<imgsrc ="doc/landcover.png"/>
- Image enhancement (Enhancement of Sentinel-2 images at 1.5m using SRGAN)
<imgsrc ="doc/supresol.png"/>
You can read more details about these applications on [this blog](https://mdl4eo.irstea.fr/2019/)
# How to install
This remote module has been tested successfully on Ubuntu 18 and CentOs 7 with CUDA drivers.
This remote module has been tested successfully on Ubuntu 18 and CentOs 7 with last CUDA drivers.
## Build OTB
First, **build the latest *develop* branch of OTB from sources**. You can check the [OTB documentation](https://www.orfeo-toolbox.org/SoftwareGuide/SoftwareGuidech2.html) which details all the steps, if fact it is quite easy thank to the SuperBuild.
A complete tutorial is available at [MDL4EO's blog](https://mdl4eo.irstea.fr/2019/01/04/an-introduction-to-deep-learning-on-remote-sensing-images-tutorial/)
# Contact
You can contact Rémi Cresson if you have any issues with this remote module at remi [dot] cresson [at] irstea [dot] fr
You can contact Remi Cresson if you have any issues with this remote module at remi [dot] cresson [at] irstea [dot] fr