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>* >
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