Abstract
Abstract. Railroad environments are peculiar, as they combine dense urban areas, along with rural parts. They also display a very specific spatial organization. In order to monitor a railway network a at country scale, LiDAR sensors can be equipped on a running train, performing a full acquisition of the network. Then most processing steps are manually done. In this paper, we propose to improve performances and production flow by creating a classification of the acquired data. However, there exists no public benchmark, and little work on LiDAR data classification in railroad environments. Thus, we propose a weakly supervised method for the pointwise classification of such data. We show that our method can be improved by using the l0-cut pursuit algorithm and regularize the noisy pointwise classification on the produced segmentation. As production is envisaged in our context, we designed our implementation such that it is computationally efficient. We evaluate our results against a manual classification, and show that our method can reach a FScore of 0.96 with just a few samples of each class.
Highlights
Railway networks are widely developed in many countries, allowing for fast and reliable people transportation, as well as freight transportation
We propose to view the problem of infrastructure detection in rail corridors as a classification problem, where each point of a LiDAR acquisition belongs either to an object of interest or to a dedicated class containing all objects not related to railroad infrastructure
We investigated the classification of LiDAR data in railroad environments
Summary
Railway networks are widely developed in many countries, allowing for fast and reliable people transportation, as well as freight transportation. In order to serve entire countries, railway network grew rapidly in the 20th century, culminating at 400000 kms for the US, 150000 kms in Russia, 64000 kms for Germany or 42000 kms for France according to the International Union of Railways Monitoring such network is costly both in terms of time and money. The monitoring of railway environments is divided in several tasks, starting by the detection of the key elements in railroad infrastructures. This can be done through camera-based data (Banicet al., 2019), or LiDAR-based data (Stein et al, 2016).
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