Active Learning for Road Lane Landmark Inventory with Random Forest in Highly Uncontrolled LiDAR Intensity Based Image

Izquierdo, Asier; López-Guede, José Manuel

doi:10.1007/978-3-030-57802-2_83

Cited by 1 publication

(1 citation statement)

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“…In addition, vehicles appearing in the image usually have highly reflective properties, making road classification difficult for RF classifiers [8]. Recently, lidar point cloud intensity data have been proposed for road landmark inventory with active learning [285].…”

Section: Roadsmentioning

confidence: 99%

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

et al. 2021

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Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.

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Section: Roadsmentioning

confidence: 99%