Forest/rural road network detection and condition monitoring based on satellite imagery and deep semantic segmentation

Sustainable forest and emergency management require comprehensive data on the forest road network and its condition. This paper presents the final framework of the INFOROAD project (https://inforoad.karteco.gr/), which integrates cutting-edge remote sensing and machine learning technologies for automated periodic extraction and monitoring of the forest road network. The framework includes gravel road extraction, road graph extraction, and gravel road condition monitoring, with a focus on the periurban forest in Thessaloniki. The road extraction employs the U-TAE network architecture, with a proposed modification using inverted residual blocks for improved accuracy. Road graph extraction involves creating a graph from road segmentation output or OSM data, enabling efficient road segment analysis. Gravel-road width calculation utilizes road segmentation results and a series of image processing steps, while road condition monitoring employs ML/AI classification algorithms. Worldview 3 high-resolution satellite images and various auxiliary data sources (e.g. DEM) are used as input, including field measurements for the training of classification algorithms. Results showcase the effectiveness of the proposed framework, with gravel road extraction accuracy improved by the modified U-TAE model. Regarding gravel road condition monitoring, algorithms achieving satisfactory results are identified, despite the challenges that arise, due to the significant surface and texture variations in forest and agricultural roads. A WebGIS platform facilitates information presentation, user interaction, and management of geospatial information, supporting various functionalities such as layer management and spatial data visualization. The INFOROAD project represents a significant advancement in leveraging technology for sustainable forest road management and emergency preparedness. Future steps may involve further enhancements and adaptations for improvement of results.