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

[Akbar Gumbira]

Cool. If you already have something in mind with QGIS, you can post your
proposal here qgis-developer at lists.osgeo.org

Cheers

On Thu, Mar 22, 2018 at 10:58 PM, Evandro Carrijo <evandro.taquary at gmail.com
> wrote:

> Hi Akbar,
>
> The idea of implementing the RNN inside a Python plugin for QGIS is great.
> Beside my experience as a QGIS user, there's already a QGIS plugin
> <https://plugins.qgis.org/plugins/LAPIGTools/> developed by the lab where
> I work, so I can take advantage of their expertise. I'll seriously consider
> this possibility.
>
> About the use of the temporal series, the information held in time between
> observations has a great value for some targets. For example, it is quite
> difficult to discriminate natural grasslands and pasturelands when relying
> only on spatial information, but, in the other hand, the temporal signature
> of the pixels holds precious information to classify between those two
> classes. Other example is the identification of different land uses such
> sugar cane and crops, which present big spectral variety in time and can
> easily mislead a spatial model. All those targets are of great interest for
> research in Brazil, and I am working on models trained on that kind of
> classes.
>
> Cheers, and thank you for the idea.
>
> 2018-03-22 17:50 GMT-03:00 Akbar Gumbira <akbargumbira at gmail.com>:
>
>> 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
>>>
>>> _______________________________________________
>>> SoC mailing list
>>> SoC at lists.osgeo.org
>>> https://lists.osgeo.org/mailman/listinfo/soc
>>>
>>
>>
>>
>> --
>>
>> *Akbar Gumbira *
>> *www.akbargumbira.com <http://www.akbargumbira.com>*
>>
>
>


-- 

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