Deep learning algorithms appear as a major breakthrough in GIS scope: neural networks are able to do semantic segmentation on aerial images, so as to identify building footprints, roads, and so on.
Oslandia is an opensource company studying and exploiting geospatial data, with an extensive R&D activity about geospatial data science. This presentation will detail some of our Python routines in terms of geospatial data handling.
We will describe our processes from raw data to prediction results. As the main step of the pipeline, machine learning techniques (e.g. convolutional neural networks for image semantic segmentation with Keras) produce valuable predictions. In the case of geospatial data, a postprocessing step is often necessary for displaying the results in web applications and GIS tools.
A concrete illustration of our results will be provided through a light Flask application designed for demonstration purpose.