2D semantic segmentation
2D semantic segmentation trained on the Vaihingen dataset
Model | Trainable | Inference | Pre-trained
Published by
DEEP-Hybrid-DataCloud Consortium
Created:
- Updated:
Model Description
Example application for ISPRS 2D Semantic Labeling Contest [1]:
2D semantic segmentation (Vaihingen dataset [2]) that assigns labels to multiple object categories.
Vaihingen dataset
- 33 patches of different sizes with 9 cm spatial resolution
- Manually classified into six land cover classes:
- Impervious surfaces, Building, Low vegetation, Tree, Clutter/background
- The groundtruth is provided for only 16 patches
- For the remaining scenes it is unreleased and used for evaluation of submitted results
N.B.: pre-trained weights can be found here (unzip before use!)
References
[1] http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html
[2] M. Cramer: The DGPF-Test on Digital Airborne Camera Evaluation Overview and Test Design, PFG Photogrammetrie, Fernerkundung, Geoinformation, vol. 2010, no. 2, pp. 73-82, 2010.
Test this module
You can test and execute this module in various ways.
Excecute locally on your computer using Docker
You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:
$ docker pull deephdc/deep-oc-semseg_vaihingen
$ docker run -ti -p 5000:5000 deephdc/deep-oc-semseg_vaihingenExecute on your computer using udocker
If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:
$ virtualenv udocker
$ source udocker/bin/activate
(udocker) $ pip install udocker
(udocker) $ udocker pull deephdc/deep-oc-semseg_vaihingen
(udocker) $ udocker create deephdc/deep-oc-semseg_vaihingen
(udocker) $ udocker run -p 5000:5000 deephdc/deep-oc-semseg_vaihingen
In either case, once the module is running, point your browser to
http://127.0.0.1:5000/ and you will see the API
documentation, where you can test the module functionality, as well as
perform other actions (such as training).
For more information, refer to the user documentation.
Train this module
You can train this model using the DEEP framework. In order to execute this module in our pilot e-Infrastructure you would need to be registered in the DEEP IAM.
Once you are registedered, you can go to our training dashboard to configure and train it.
For more information, refer to the user documentation.