[SoC] 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 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 or a OSGeo Project suitable to it.

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 a Sentinel time-series, that will organize and preprocess
an 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.

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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