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The Repository is ARCHIVED!

it is now maintained in https://github.com/ai4os-hub/semseg-vaihingen/

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2D semantic segmentation

2D semantic segmentation (Vaihingen dataset)

Author: G.Cavallaro (FZJ), M.Goetz (KIT), V.Kozlov (KIT), A.Grupp (KIT), S.Donayer Holz (KIT)

Project: This work is part of the DEEP Hybrid-DataCloud project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

This is an example application for ISPRS 2D Semantic Labeling Contest : "2D semantic segmentation (Vaihingen dataset) that assigns labels to multiple object categories.",

You can find more information about it in the DEEP Marketplace.

Table of contents

1. Installing this module
   1. Local installation
   2. Docker installation
2. Train the classifier
   1. Data preprocessing
   2. Train the classifier
3. Predict
4. Acknowledgements

Installing this module

Local installation

Requirements

This project has been tested in Ubuntu 18.04 with Python 3.6. Further package requirements are described in the requirements.txt file.

To start using this framework clone the repo:

git clone https://github.com/deephdc/semseg_vaihingen
cd semseg_vaihingen
pip install -e .

now run DEEPaaS:

deepaas-run --listen-ip 0.0.0.0

and open http://0.0.0.0:5000/ui and look for the methods belonging to the semseg_vaihingen module.

Docker installation

We have also prepared a ready-to-use Docker container to run this module. To run it:

docker search deephdc
docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 deephdc/deep-oc-semseg_vaihingen

Now open http://0.0.0.0:5000/ui and look for the methods belonging to the semseg_vaihingen module.

Train a the classifier

Data preprocessing

The first step to train the neural network is to put the training file vaihingen_train.hdf5 and the validation file vaihingen_val.hdf5 into ./semseg_vaihingen/data. More information about how to acquire the vaihingen dataset can be found here.

An example script on how one may convert raw data into hdf5 file is also provided in semseg_vaihingen/dataset/raw2hdf5.py.

Train the classifier

Go to http://0.0.0.0:5000/ui and look for the TRAIN POST method. Click on 'Try it out', change whatever training args you want and click 'Execute'. The training will be launched and you will be able to follow its status by executing the TRAIN GET method which will also give a history of all trainings previously executed.

If the module has some sort of training monitoring configured (like Tensorboard) you will be able to follow it at http://0.0.0.0:6006.

After training you can check training statistics and check the logs where you will be able to find the standard output during the training together with the confusion matrix after the training was finished.

Predict

This module comes with a pretrained classifier so you won't have to train the classifier first before being able to use the testing methods.

Go to http://0.0.0.0:5000/ui and look for the PREDICT POST method. Click on 'Try it out', change whatever test args you want and click 'Execute'. You must supply a data argument with a path pointing to a vaihingen_#.hdf5 or any .tiff, .png, .jpg file. You can also choose either getting the results as a json response or as a downloadable .pdf file.

Acknowledgments

If you consider this project to be useful, please consider citing the DEEP Hybrid DataCloud project:

García, Álvaro López, et al. A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access 8 (2020): 18681-18692.