Paper
21 September 2023 A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation
Author Affiliations +
Proceedings Volume 12786, Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023); 127860F (2023) https://doi.org/10.1117/12.2681850
Event: Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023, Ayia Napa, Cyprus
Abstract
The role of forest and rural road network is very important and multifaceted for: a) the exploitation of forests/fields and movement of the staff involved in their management and protection, b) access to existing technical facilities, c) tourism and recreation and d) harvesting and transport of products. Hence, it is important to systematically record and monitor the network status, including its road segment status and important Points of Interest. Since in most cases, such detection and monitoring procedures require manual and tedious procedures, the INFOROAD project (https://inforoad.karteco.gr/) (Project code: KMP6-0079153), implemented under the framework of the Action “Investment Plans of Innovation” of the Operational Program “Central Macedonia 2014 2020”, that is co-funded by the European Regional Development Fund and Greece”, is developing an innovative methodology and tools for automated extraction, mapping and status monitoring of forest and rural road network, by combining remote sensing data with state-of-the-art deep learning approaches. Specifically, automated procedures will be developed for: a) road extraction, b) road network graph detection and c) road segment condition monitoring, from very high-resolution multispectral satellite images using innovative semantic segmentation and classification based on deep learning. From a research perspective, these problems are very challenging, as forest and agricultural roads – as opposed to asphalt roads - exhibit more significant surface and texture variations, while only few related datasets exist. For this reason, additional data, which is necessary for the training and evaluation of the deep learning algorithms, will also be collected, e.g., by UAVs equipped with RGB cameras that can also provide accurate DSM information, manual inspection or other free sources. The associated dataset that will be created for the pilot application of the project in Seich-Sou Forest, Thessaloniki, will be made freely available to boost future research on the field. The display and management of this information by relevant stakeholders (forest authorities, public, etc.) will be performed by an online Geographical Information System (WebGIS).
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dimitrios Kelesakis, Konstantinos Marthoglou, Nikos Grammalidis, Petros Daras, Emmanouel Tsiros, Apostolos Karteris, and Anastasia Stergiadou "A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation", Proc. SPIE 12786, Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 127860F (21 September 2023); https://doi.org/10.1117/12.2681850
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KEYWORDS
Roads

Image segmentation

Satellite imaging

Data modeling

Earth observing sensors

Semantics

Deep learning

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