Automated Detection of Pedestrian and Bicycle Lanes from High-Resolution Aerial Images by Integrating Image Processing and Artificial Intelligence (AI) Techniques

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Detalles Bibliográficos
Publicado en:	ISPRS International Journal of Geo-Information vol. 14, no. 4 (2025), p. 135
Autor principal:	Antwi Richard Boadu
Otros Autores:	Lawson Prince Lartey, Kimollo, Michael, Ozguven Eren Erman, Ren, Moses, Dulebenets, Maxim A, Sando Thobias
Publicado:	MDPI AG
Materias:	Florida United States > US Bicycles Identification methods Artificial neural networks Roads & highways Transportation networks Transportation planning Image processing Traffic flow Computer vision Machine learning Energy consumption Imagery Efficiency Global positioning systems > GPS Spatial data Ground truth High resolution Traffic control Artificial intelligence Rural areas Algorithms Inventory management Inventory Urban transportation Accuracy Deep learning Image resolution Models Pedestrians Automation Bicycling Infrastructure Road design Traffic accidents & safety Data collection Industrial safety Urban areas Satellites Neural networks
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks.
ISSN:	2220-9964
DOI:	10.3390/ijgi14040135
Fuente:	Advanced Technologies & Aerospace Database