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 <direg...@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.taqu...@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 >> s...@lists.osgeo.org >> https://lists.osgeo.org/mailman/listinfo/soc >> > > > > -- > Margherita Di Leo >
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