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 <[email protected]> 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 <[email protected]>: > >> 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 < >> [email protected]> 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 >>> [email protected] >>> 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>* >> > > > _______________________________________________ > QGIS-Developer mailing list > [email protected] > List info: https://lists.osgeo.org/mailman/listinfo/qgis-developer > Unsubscribe: https://lists.osgeo.org/mailman/listinfo/qgis-developer >
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