Automatic forest road extraction using a deep-learning model with a CS topographical map

Abstract

Naoki Aihara and Yasushi Mitsuda: Automatic forest road extraction using a deep-learning model with a CS topographical map. Jpn. Jpn. J. For. Plann. 59: 11～17, 2025　Utilizing a CS topographical map derived from a Digital Elevation Model (DEM) acquired through airborne laser scanning (ALS), we developed a methodology to automatically extract forest roads using a deep learning model. The deep-learning model, employing a CS topographical map as the input, reproduced 75.1% of the route length for actual forest roads. Of the forest roads extracted by the model, 91.7% were consistent with the existing forest roads. In contrast, the deep-learning model developed using elevation, slope, and plane curvature as inputs could reproduce only 0.1% of the route length for actual forest roads. In forested areas, the automatic extraction of forest roads using conventional photographs is challenging due to vegetation cover. Our forest road detection model can overcome this limitation because it utilizes an ALS-derived DEM as the input. Furthermore, the reproduction rate of forest roads in our model employing the CS topographical map as the input image was significantly higher than that of the model that used elevation, slope, and plane curvature as the input images. This result indicates the efficacy of the CS topographical map for automatic forest road extraction using deep-learning models.