[QGIS-Developer] Just in time: Seeking mentors for development of a Deep Learning model applied to Remote Sensing Data

[Evandro Carrijo]

Hello there!

I'm a Computer Science Master's Degree student whose research if focused on
Deep Learning algorithms applied to Remote Sensing. Currently working at
the Laboratory of Image Processing and Geoprocessing
<https://github.com/lapig-ufg> settled at Federal University of Goiás -
Brazil. I'm also member of the High Performance Computing group of the same
university (more information here
<http://dgp.cnpq.br/dgp/espelhogrupo/7985061476854055>).

Below I present an idea to explain how I can contribute to OSGeo/QGIS
community and I'm seeking for mentors interested in assist my development.
Please, feel free to argue me any matter about the project idea.

I would also appreciate a lot if you guys indicate a potential interested
mentor to my project idea.

Hope there's some Interested ones out there!

Idea

The increasing number of sensors orbiting the earth is systematically
producing larger volumes of data, with better spatiotemporal resolutions.
To deal with that, better accurate machine learning approaches, such as
Deep Learning (DL), are needed to transform raw data into applicable
Information. Several DL architectures (e.g. CNN, semantic segmentation)
rely only at spatial dimension to perform, for example, land-cover/land-use
(LCLU) maps, disregarding the temporal dependencies between pixels
observations over the time. Also, high-res remote sensing data (e.g.
Planet, Sentinel) may provide more consistent time-series, that can be use
in the identification of important LCLU classes, like crop, pastureland and
grasslands.

This potential can be explored using Recurrent Neural Networks (RNN), a
specific family of DL approaches which can take into account time
dimension. A promising project idea would be implement a RNN approach (e.g.
LSTM) to classify, for example, a Sentinel time-series, that will organize
and preprocess the input data set (e.g. labeled time-series), calibrate and
evaluate a RNN model, and finally classify an entire region (i.e. 2 or 3
scenes) to produce a map for one or more LCLU class. It will be great
evaluate the accuracy and the spatial consistent of a map produced with a
RNN approach.

The RNN model can then be shipped into a QGIS Plugin with a convenient
interface such that one could accomplish the following tasks:


   - Select the input data;
   - Adjust some model hyperparameters (if desirable);
   - Train the RNN;
   - Export the generated model for persistence;
   - Use the model to produce a LCLU map for the specified targets.

The idea is to start a new Plugin that use not only RNN models, but, in the
future, incorporate many other novel approaches to perform accurate LCLU
maps, like semantic segmentation using U-Nets and a combination of the two
approaches.

A simple example on classifying LCLU with two classes (pastureland and
non-pastureland):

[image: itapirapua]
<https://user-images.githubusercontent.com/37085598/37687055-cc5236a8-2c78-11e8-8892-d113df44e235.jpg>
*Target region (input)*

[image: itapirapua_ref]
<https://user-images.githubusercontent.com/37085598/37732806-ec792782-2d24-11e8-8ad9-18867768e998.jpg>
*Generated LCLU map (output)*
Best,

Evandro Carrijo Taquary
Federal University of Goiás
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