Использование данных дистанционного зондирования Земли из космоса для распознавания изображения дорог в лесном хозяйстве

Вопросы лесной науки. 2022. Т. 5. № 4. Статья 115.

Podolskaia E.

Paper presents an overview of history and current research state on the use of remote sensing data from
space to recognize roads for the regional projects. We have characterized principles of road detection on the im-
agery. A group of direct deciphering signs used in combinations such as brightness and texture, geometry and
brightness. Three research directions with examples identified: visual roads recognition, use of special software
and libraries for developers, and use of neural networks. For the road network detection we have described meth-
ods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from
the open and commercial sources, machine learning methods and neural networks. Actual tasks of road recogni-
tion are the following: evaluation of road surface condition, modeling of existing roads location, designing and
building new roads, seasonality of roads use. A functionality summary of MapFlow plugin for road recognition in
Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires
and forest resources by ground means.

Keywords: лесное хозяйство, Open Source, нейросеть, дистанционное зондирование Земли, QGIS, дорожная сеть