Forest road detection using deep learning models

Çalışkan E., Sevim Y.

Geocarto International, vol.37, no.20, pp.5875-5890, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 20
  • Publication Date: 2022
  • Doi Number: 10.1080/10106049.2021.1926555
  • Journal Name: Geocarto International
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Environment Index, Geobase, INSPEC
  • Page Numbers: pp.5875-5890
  • Keywords: Forest road extraction, orthophotos, remote sensing, deep learning, semantic segmentation, BUILDING DETECTION, EXTRACTION, IMAGES, FRAMEWORK, NETWORK, SEGMENTATION
  • Karadeniz Technical University Affiliated: Yes


© 2021 Informa UK Limited, trading as Taylor & Francis Group.Forest roads are the primary infrastructure facilities of forestry activities. Identifying the forest roads that can be used after a disaster is very important for management of disasters such as fire, flood, and landslide as well as for strengthening of the forest road infrastructure. In this context, forest road extraction from orthophoto images has become a hot research topic in the field of remote sensing image analysis. Deep learning methods stand out in many fields that require remote sensing and these methods lead to very successful results compared with traditional methods. Recently, deep learning methods are applied frequently in road extraction. The aim of the present study was forest road network extraction from high resolution orthophoto images based on deep learning. Four different deep learning models have been used in the study which are AlexNet, ResNet-50, InceptionResNet-V2 and U-Net. First, the images in the dataset were subject to re-processing after which the deep learning models were trained separately. Secondly, values of overall accuracy, precision, recall, Dice coefficient, intersection over union, and test time were calculated for these trained network models over the validity dataset. Finally, the acquired results were compared and forest road segmentation inferences were visualized thus putting forth the accuracy at which the deep learning models used can extraction the forest roads. The results show that ResNet-50 and InceptionResNet-V2 semantic segmentation models can be used accurately and efficiently for forest road extraction.