Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios.

High-precision on-orbit geometric calibration of spaceborne laser altimetry data is essential to its effective applications. Firstly, the existing calibration methods for laser altimeter data are analyzed. Then, a geometric calibration method based on curve matching is proposed. Compared to the existing methods, the proposed method does not rely on ground calibration field. Thus, it is efficiency in expense and time. Notably, three factors, i.e. matching method, initial control point selection and the step size of matching step, which significantly affect the results of calibration are analyzed respectively. The analysis was validated based on the original laser altimetry data obtained by ZY3-02 satellite. According to the results, the following conclusions can be drawn preliminarily: (1) Both the correlation coefficient maximum (COR) criterion and the mean square error minimum (MSD) criterion in the curve matching can be used to correct the systematic error in altimetry data. (2) The initial control points of the selected track should have a significant change trend and the slope within the laser footprints should be less than 15°. (3) Current experimental data show that the best step size for matching search is 10 m. The relevant conclusions can provide reference for the research of geometrical calibration and data processing of the same type of laser altimetry satellite.