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

[Akbar Gumbira]

Hi,

It sounds like a nice idea. IMO you can also implement it as a python
plugin for QGIS. If you need a simple RNN implementation, you can use
Pyrenn (http://pyrenn.readthedocs.io/en/latest/) and ship it with the
plugin.

Just wondering, why does it need to consider the temporal information?
What's the relevance of the history of a spatial area to the
classification? Shouldn't it just classify based on the latest data?

Cheers

On Thu, Mar 22, 2018 at 5:49 PM, Evandro Carrijo <evandro.taquary at gmail.com>
wrote:

> 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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>



-- 

*Akbar Gumbira *
*www.akbargumbira.com <http://www.akbargumbira.com>*
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