APLS Plugin for QGIS Software

QGIS Plugin for Extracted roads evaluation using APLS matrix.

The APLS Plugin for QGIS Software is a tool that can be used to evaluate extracted road networks from satellite imagery. This plugin uses the Average Path Length Similarity (APLS) matrix to compare the extracted road network to a ground truth network. APLS is a measure of the similarity between two networks, based on the average shortest path length between pairs of nodes.

Requirements

QGIS 3.0 or higher

Usage

Before openning the plugin make sure Python console is opened
Run the plugin and specify inputs correctly

ex:Ground truth: /main_folder/Predicted_shp/
Predicted : /main_folder/test_shp/
Djkstra Tolerance : 0.0
Snapping Tolerance : 8.0

Run and wait untill processing is finished
The results are displayed in Python console

Results example :

Processing total of : 49 images.
Apls for Folder : 18178780_15  is  0.9886363636363636
Apls for Folder : 11278840_15  is  0.11675731021559
Apls for Folder : 23278915_15  is  0.3240173376386126
Apls for Folder : 18478900_15  is  0.9666666666666667
................
Apls for Folder : 24628885_15  is  0.07408254856578367
Apls for Folder : 22228900_15  is  0.4204545454545454
Apls for Folder : 24479215_15  is  0.1971564400253033
AVG APLS is 0.4584244128431006

Inputs

Ground truth folder contains sub-folders each sub-folder contain shapefile
Predicted network folder which contains sub-folders each sub-folder contain shapefile
Djkstra Algorithm Tolerance
Snapping Tolerance

Example of Folder Structure for the files

The plugin requires QGIS 3.0 or higher to be installed and allows users to input a ground truth folder containing sub-folders, each of which contains a shapefile, and a predicted network folder containing sub-folders, each of which also contains a shapefile. The plugin then calculates the APLS for each sub-folder in the predicted network folder and outputs the results in the Python console.

/main_folder
│   └── test_shp/
│       ├── 10378780_15
│       │   ├── 10378780_15.cpg
│       │   ├── 10378780_15.dbf
│       │   ├── 10378780_15.prj
│       │   ├── 10378780_15.shp
│       │   ├── 10378780_15.shx
│       ├── 10828720_15
│       │   ├── 10828720_15.cpg
│       │   ├── 10828720_15.dbf
│       │   ├── 10828720_15.prj
│       │   ├── 10828720_15.shp
│       │   ├── 10828720_15.shx
-----------------------------------
│   └── Predicted_shp/
│       ├── 10378780_15
│       │   ├── 10378780_15.cpg
│       │   ├── 10378780_15.dbf
│       │   ├── 10378780_15.prj
│       │   ├── 10378780_15.shp
│       │   ├── 10378780_15.shx
│       ├── 10828720_15
│       │   ├── 10828720_15.cpg
│       │   ├── 10828720_15.dbf
│       │   ├── 10828720_15.prj
│       │   ├── 10828720_15.shp
│       │   ├── 10828720_15.shx

References


@inproceedings{van2020road,
  title={Road network and travel time extraction from multiple look angles with spacenet data},
  author={Van Etten, Adam and Shermeyer, Jacob and Hogan, Daniel and Weir, Nicholas and Lewis, Ryan},
  booktitle={IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium},
  pages={3920--3923},
  year={2020},
  organization={IEEE}
}


Citation

@masterthesis{MohammedThesisRoad,
    author = {Mohammed Nasser},
    title = {Road Identification from Satellite Imagery Using Deep Learning},
    school = {Erciyes University},
    year = {2022}
}