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

Fri Mar 23 08:20:45 PDT 2018

Evandro,

This is a research interest of mine. I would be happy to mentor or
otherwise be involved. I've been an SOC mentor a few times in the past.
This looks quite promising.

THK

http://www.keittlab.org/

On Fri, Mar 23, 2018 at 9:35 AM, Evandro Carrijo <evandro.taquary at gmail.com>
wrote:

> 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
>>>
>>> _______________________________________________
>>> QGIS-Developer mailing list
>>> QGIS-Developer at lists.osgeo.org
>>> List info: https://lists.osgeo.org/mailman/listinfo/qgis-developer
>>> Unsubscribe: https://lists.osgeo.org/mailman/listinfo/qgis-developer
>>>
>>
>>
>>
>> --
>>
>> *Akbar Gumbira *
>> *www.akbargumbira.com <http://www.akbargumbira.com>*
>>
>
>
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