1. Run sample project#

Once the Installation of orthoseg and its dependencies is completed, you can run the sample project included. The sample project is an easy way to get started and should give a good idea on how you can start your own segmentation project.

It contains:

a training dataset that can be used to train a network to detect football fields
a sample of the basic configuration for a typical project that specifies using a lightweight neural network so it should run smoothly on a regular computer
a sample of the default directory structure used by orthoseg
a QGIS project file with the training data + aerial images that will be used to train the neural network + to detect football fields on

Note

The training data included and pretrained model based on it is meant to show how the process works, not to give perfect, or even decent, results.

Running the sample project is easy. If the installation was successful, the following steps should do the trick:

start a conda command prompt
activate the orthoseg environment with:
```
conda activate orthoseg
```
download the sample projects from orthoseg. You can specify the base location to download the sample projects to, but in this tutorial I’ll assume “~” (= your home directory) for simplicity:
```
orthoseg_load_sampleprojects ~
```
preload the images so they are ready to detect the football fields on, using the sample configurations file “footballfields.ini_BEFL-2019”:
```
orthoseg_load_images --config ~/orthoseg/sample_projects/footballfields/footballfields_BEFL-2019.ini
```
for the footballfields sample project, a pretrained neural network was downloaded in step orthoseg_load_sampleprojects to avoid having to train it. But, normally you would now train the neural network with the following command:
```
orthoseg_train --config ~/orthoseg/sample_projects/footballfields/footballfields.ini
```

detect the football fields:

orthoseg_predict --config ~/orthoseg/sample_projects/footballfields/footballfields_BEFL-2019.ini

Now, the directory ~/orthoseg/sample_projects/footballfields/output_vector will contain a .gpkg file with the football fields found.

An interesting exercise might be to detect football fields on another layer (on another location). To get reasonable results, this should be a layer with 0.25 meter pixel size, as this was the pixel size the footballfields detection was trained on. It’s best to first read Prepare a new project for some background information and then you could try the following steps:

Add the layer you want to predict on to the imagelayer.ini config file located in your projects directory (~/orthoseg/sample_projects). Not a very interesting example, but if you don’t have a lot of inspiration you could use a layer with orthophotos of 2020 on the same location as the 2019 layer above. This can be configured as follows:
```
[BEFL-2020]
wms_server_url = https://geo.api.vlaanderen.be/omw/wms?
wms_layernames = OMWRGB20VL
wms_layerstyles = default
wms_version = 1.3.0
projection = epsg:31370
bbox = 174900, 176400, 175300, 176600
```
Detailed information on the different available options to configure image layers can be found in Image Layers Configuration.
Make a copy of footballfields_BEFL-2019.ini and change the predict image_layer parameter in the file to point to the new layer, e.g.:
```
[predict]
image_layer = BEFL-2020
```
As an alternative to having to create a specific .ini file for each layer you want to predict on, you can also overrule a key (or keys) by passing overrules as extra parameters to e.g. orthoseg_predict. For this case, to overrule predict.image_layer, you can add parameter predict.image_layer=BEFL-2020:
```
orthoseg_predict --config ~/orthoseg/sample_projects/footballfields/footballfields.ini predict.image_layer=BEFL-2020
```

Run orthoseg_load_images to prepare the layer to predict on:

orthoseg_load_images --config ~/orthoseg/sample_projects/footballfields/footballfields_BEFL-2020.ini

Run the detection again with orthoseg_predict:

orthoseg_predict --config ~/orthoseg/sample_projects/footballfields/footballfields_BEFL-2020.ini