Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys

Thegeya, Aaron; Mitterling, Thomas; Martínez, A. J.; Bulan, Joseph Albert Niño; Durante, Ron Lester; Mag-atas, Jayzon

doi:10.22617/wps220587-2

Cited by 1 publication

(1 citation statement)

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“…A similar approach is used in, 12 where training using two deep learning architectures, Vgg16 and ResNet50 was used. Also, 13 used IRI measurement data for roads in the Philippines, Sentinel-2 satellite imagery as well as a custom architecture including ResNet-34 CNN to classify road quality into four classes. A transfer learning approach is proposed by, 14 where a CNN neural network is first trained on road quality data collected in the United States (where the data is readily available) and then fine-tuned on an independent, smaller data set collected from Nigeria.…”

Section: Related Workmentioning

confidence: 99%

A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation

Kelesakis,

Marthoglou,

GRAMMALIDIS

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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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).

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Section: Related Workmentioning

confidence: 99%