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

[Evandro Carrijo]

Hi Margherita,

I really appreciate for your feedback! I'm not much familiar with GRASS GIS
as I had only developed standalone codes in Python using directly libraries
like GDAL and RIOS <http://rioshome.org> and used QGIS for layers
visualization. But, as I observed here
<http://grass.osgeo.org/programming6/pythonlib.html> and here
<https://grasswiki.osgeo.org/wiki/GRASS_and_Python>, Python scripts can
easily be integrated within GRASS GIS and I could seamlessly adapt my
programming skills to work with that. That way, I could compromise myself
in integrating my already done codes (and new ones) into GRASS GIS
software/libraries.

Also, if allowed, I could edit the Ideas' wiki page to contemplate my own
idea, so that it could be visible to a broader audience. If I get a mentor
in time, I will make a detailed proposal for the mentors/community be able
to understand better the idea.

Thank you very much,

Evandro Carrijo Taquary
Federal University of Goiás

2018-03-22 14:08 GMT-03:00 Margherita Di Leo <diregola at gmail.com>:

> 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
>>
>> _______________________________________________
>> SoC mailing list
>> SoC at lists.osgeo.org
>> https://lists.osgeo.org/mailman/listinfo/soc
>>
>
>
>
> --
> Margherita Di Leo
>
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