Abstract
Abstract. Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%.
Highlights
In the last years, governments and other institutions worldwide have been promoting the survey of large areas of the national territory to be employed, e.g., for natural hazard management, urban planning and facilities monitoring
We show how an algorithm based on Convolutional Neural Networks (CNNs) was profitably employed to classify a dataset of 7700 km2, that covers the entire Friuli Venezia Giulia region (Italy)
The proposed approach is applied to distinguish among five classes, namely: ground, vegetation, roof, overground and power line, achieving an overall accuracy of 92.9% with a classification time of 11 minutes per km2
Summary
Governments and other institutions worldwide have been promoting the survey of large areas of the national territory to be employed, e.g., for natural hazard management, urban planning and facilities monitoring. In this context, airborne LiDAR (Light Detection and Ranging) technology represents a suitable survey platform to obtain high resolution data at wide scale, requiring, efficient algorithms to handle and process the large amount of acquired data. The proposed approach is applied to distinguish among five classes, namely: ground, vegetation, roof, overground (e.g., cars, walls and chimneys) and power line, achieving an overall accuracy of 92.9% with a classification time of 11 minutes per km. Our method achieved a good classification in mountainous environments
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