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

[Evandro Carrijo]

Hello Akbar,

This is an important remark. The Plugin could provide three scenarios at
all:

   1. Train the model from scratch (when there's sufficient computational
   resources);
   2. Use Transfer Learning, that is, use models with pre-trained weights;
   3. Like you suggest, fetch the desired model from a *zoo platform* suitable
   to his data.

An important caveat, although, is that the models can be very
region-specific, that is when the scenarios 1 and 2 are applicable. Also,
user can fetch well consolidated models from the *model zoo platform* as
basis and tune their models as of them.

I'm going to write my proposal right away, so that ideas are going to take
place. As soon as I have the first version of it I will share with you guys.

Thank your for you precious advices!

Cheers

2018-03-23 11:12 GMT-03:00 Akbar Gumbira <akbargumbira at gmail.com>:

> Hi (again),
>
>
>> The RNN model can then be shipped into a QGIS Plugin with a convenient
>> interface such that one could accomplish the following tasks:
>>
>>    - Select the input data;
>>
>>
>>    - Adjust some model hyperparameters (if desirable);
>>
>>
>>    - Train the RNN;
>>
>>
>>    - Export the generated model for persistence;
>>
>>
>>    - Use the model to produce a LCLU map for the specified targets.
>>
>> The idea is to start a new Plugin that use not only RNN models, but, in
>> the future, incorporate many other novel approaches to perform accurate LCLU
>> maps, like semantic segmentation using U-Nets and a combination of the two
>> approaches.
>>
> Not everyone probably wants (or has the resources) to train the data. Why
> not, for example, have a model zoo platform where users can share their
> models for particular defined classifications? or will the training always
> be lightweight and instant?
>
> Cheers
>
>
> On Fri, Mar 23, 2018 at 2:52 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/QGIS
>> 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.
>>
>> 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, for example, a Sentinel time-series, that will organize
>> and preprocess the 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.
>>
>> The RNN model can then be shipped into a QGIS Plugin with a convenient
>> interface such that one could accomplish the following tasks:
>>
>>
>>    - Select the input data;
>>    - Adjust some model hyperparameters (if desirable);
>>    - Train the RNN;
>>    - Export the generated model for persistence;
>>    - Use the model to produce a LCLU map for the specified targets.
>>
>> The idea is to start a new Plugin that use not only RNN models, but, in
>> the future, incorporate many other novel approaches to perform accurate LCLU
>> maps, like semantic segmentation using U-Nets and a combination of the two
>> approaches.
>>
>> 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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