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Related Experiment Video

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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Utilizing High Resolution Satellite Imagery for Automated Road Infrastructure Safety Assessments.

Ivan Brkić¹, Marko Ševrović², Damir Medak¹

¹Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This study introduces an automated method using satellite imagery and the Yolo object detector to identify school zones, crosswalks, and divided carriageways, enhancing road safety data collection.

Keywords:

road assessment road markings road safety satellite imagery

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Area of Science:

Geospatial analysis
Computer vision
Road safety engineering

Background:

European Union (EU) Road Safety Framework (2021-2030) mandates detailed road attribute recording.
EU Directive 2019/1936 requires enhanced data for road safety.
Need for automated methods to process large-scale geospatial data for road infrastructure.

Purpose of the Study:

To develop and evaluate an automated system for detecting key road features: school zones, four types of crosswalks, and divided carriageways.
To leverage high-resolution satellite imagery and advanced object detection algorithms for accurate road attribute mapping.
To establish benchmark performance metrics for future road safety data analysis.

Main Methods:

Utilized Pleiades Neo 3 satellite imagery (0.3 m spatial resolution).
Integrated the You Only Look Once (Yolo) object detection model.
Employed a three-phase approach: vector processing, satellite image processing, and Yolo model training/evaluation.

Main Results:

Achieved high accuracy for school zones (0.988) and divided carriageways (0.950).
Demonstrated strong performance for crosswalk detection, with accuracies ranging from 0.957 to 0.988.
Provided comprehensive performance metrics including mean average precision (mAP), confusion matrix, precision, recall, and F1 scores.

Conclusions:

The automated system effectively identifies critical road features from satellite imagery, supporting EU road safety initiatives.
The Yolo object detector, combined with high-resolution satellite data, offers a scalable solution for road attribute mapping.
The established benchmarks will facilitate future research and development in automated road safety analysis.