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

[Margherita Di Leo]

Hi Evandro,

thank you for your proposal, I put in cc also the GRASS GIS dev mailing
list, as it might be a suitable project candidate if anyone is available
for mentoring it. It is usually a bit more difficult to find mentors when
the proposal comes from a student and it is not listed in our ideas page,
however not impossible, and your idea sounds very interesting. Are you
familiar with GRASS GIS?
I'd like to point you out our recommendations for students at
https://wiki.osgeo.org/index.php?title=Google_Summer_of_Code_Recommendations_for_Students
, particularly our guidelines on how to submit a proposal.

Thanks,

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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> SoC at lists.osgeo.org
> https://lists.osgeo.org/mailman/listinfo/soc
>



-- 
Margherita Di Leo
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