Image analysis with deep learning and neural networks
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This project aims at showcasing some Deep Learning use cases in terms of image analysis, especially regarding semantic segmentation.

If you want to get more details on Oslandia activities around this topic, feel free to visit our blog. You certainly want to discover some of our results in the associated web application:

Web application homepage


The project contains the following folders:

  • deeposlandia contains the main Python modules to train and test convolutional neural networks
  • examples contains some Jupyter notebooks that aim at describing data and building basic neural networks
  • images contains some example images to illustrate the Mapillary dataset as well as some preprocessing analysis results
  • tests; pytest is used to launch several tests from this folder.

Additionally, running the code may generate extra subdirectories in the chosen data repository.



The code has been run with Python 3. All dependencies are specified in file, and additional dependencies for developing purpose are listed in requirements-dev.txt.

From source

$ git clone
$ cd deeposlandia
$ virtualenv -p /usr/bin/python3 venv
$ source venv/bin/activate
(venv)$ python install
(venv)$ pip install -r requirements-dev.txt

Running the code

Supported datasets


In this project we use a set of images provided by Mapillary, in order to investigate on the presence of some typical street-scene objects (vehicles, roads, pedestrians...). Mapillary released this dataset on July 2017, it is available on its website and may be downloaded freely for a research purpose.

As inputs, Mapillary provides a bunch of street scene images of various sizes in a images repository, and the same images after filtering process in instances and labels repositories.

There are 18000 images in the training set, 2000 images in the validation set, and 5000 images in the testing set. The testing set is proposed only for a model test purpose, it does not contain filtered versions of images. The raw dataset contains 66 labels, splitted into 13 categories. The following figure depicts a prediction result over the 13-labelled dataset version.

Example of image, with labels and predictions

AerialImage (Inria)

In the Aerial image dataset, there are only 2 labels, i.e. building or background and consequently the model aims at answering one single question for each image pixel: does this pixel belongs to a building?

The dataset contains 360 images, one half for training one half for testing. Each of these images are 5000*5000 tif images. Amongst the 180 training images, we assigned 15 training images to validation. One example of this image from this dataset is depicted below.

Example of image, with labels and predictions


To complete the project, and make the test easier, a randomly-generated shape model is also available. In this dataset, some simple coloured geometric shapes are inserted into each picture, on a total random mode. There can be one rectangle, one circle and/or one triangle per image, or neither of them. Their location into each image is randomly generated (they just can't be too close to image borders). The shape and background colors are randomly generated as well.

Flask application

A Flask Web application may be launched locally through deeposlandia/ By default, it is launched on

Some symbolic links are needed to make the application work (in development mode):

  • deeposlandia/static/sample_images must contain the sample images, depicted on application homepage as well as in demonstration web pages (before new images are generated).
  • deeposlandia/static/shapes refers to the server-side repository that contains shapes images and their labels.
  • deeposlandia/static/mapillary_agg refers to the server-side repository that contains Mapillary images and their aggregated labels, i.e. 13 labels that summarize the content of the 66 native Mapillary labels.
  • deeposlandia/static/predicted_images links to a temporary repository (for instance, /tmp/deeposlandia/predicted/) that contains images generated during the app session as well as their corresponding predicted labelled version.

These symlinks are created when the web application is launched. Their name as well as their destination are defined in config.ini, a config file located on the project root.


The program license is described in

Oslandia, April 2018