# UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks

Source: [UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks](<https://www.sota2.com/research/paper/68fa6b0d1e209131cea2bba0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:38:16.503Z

Authors: Weikai Tan, Dedong Zhang, Lingfei Ma, Ying Li, Lanying Wang, Jonathan Li

Published: 2020-10-21T16:15:41.000Z

## Abstract

Stack interchanges are essential components of transportation systems. Mobile laser scanning (MLS) systems have been widely used in road infrastructure mapping, but accurate mapping of complicated multi-layer stack interchanges are still challenging. This study examined the point clouds collected by a new Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) system to perform the semantic segmentation task of a stack interchange. An end-to-end supervised 3D deep learning framework was proposed to classify the point clouds. The proposed method has proven to capture 3D features in complicated interchange scenarios with stacked convolution and the result achieved over 93% classification accuracy. In addition, the new low-cost semi-solid-state LiDAR sensor Livox Mid-40 featuring a incommensurable rosette scanning pattern has demonstrated its potential in high-definition urban mapping.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.11106>)

[PDF](<https://arxiv.org/pdf/2010.11106.pdf>)