Road Extraction

This state-of-the-art deep learning solution has been developed to perform automatic extraction and mapping of roads from high-resolution aerial and satellite imagery with a 30-50 cm ground sample distance (GSD).

In this regard, it should be noted that road networks are considered to be one of the most important geographic features in the context of modern mapping and spatial analysis practices. They can be used in different ways ranging from urban development to infrastructure planning and transportation analysis, navigation, change detection, etc. At the same time, the generation of high-resolution road layers using conventional manual digitization might take much time and effort, especially when considering large territories.

This deep learning solution accelerates the process of road layer generation from high-resolution imagery by performing an automated extraction of road objects. Thus, instead of performing laborious tasks such as manual digitization of each individual road segment, GIS experts and analysts have an opportunity to speed up the process of generating structured data.

The model is able to interpret complex visual and spatial features of road networks, such as variations in road width, surface appearance, surrounding land cover, intersections, curvature, and densified area. The model, therefore, is ideal for processing imagery in urban, suburban, and any other built-up areas where road networks possess varying visual features.

The use of automatic road extraction could facilitate geospatial change detection whereby the differences in road networks in imagery taken at various time points would be easily established through the identification of newly constructed roads, road extensions, modifications, and alterations in transportation infrastructure.

Combining the power of deep learning, computer vision, aerial imagery, satellite imagery, and GIS, the proposed model offers a scalable solution for road network mapping. It would allow organizations to decrease manual processing tasks, increase geospatial data creation speed, and generate accurate road layers for further geospatial analysis and decision-making.

No matter what purpose – base map generation, transportation planning, urban development monitoring, infrastructure assessment, or geospatial projects on a large scale – is assigned to this automated road extraction model, it will allow one to convert high-resolution imagery into meaningful geospatial data.