Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks

被引：5

作者：

Salberg, Arnt-Borre ^{[1
]}

Trier, Oivind Due ^{[1
]}

Kampffmeyer, Michael ^{[2
]}

机构：

[1] Norwegian Comp Ctr, POB 114, N-0314 Oslo, Norway

[2] UiT Arctic Univ Norway, N-9037 Tromso, Norway

来源：

IMAGE ANALYSIS, SCIA 2017, PT II | 2017年 / 10270卷

关键词：

Deep learning; Convolutional neural networks; Lidar; Remote sensing;

D O I：

10.1007/978-3-319-59129-2_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Detailed and complete mapping of forest roads is important for the forest industry since they are used for timber transport by trucks with long trailers. This paper proposes a new automatic method for large-scale mapping forest roads from airborne laser scanning data. The method is based on a fully convolutional neural network that performs end-to-end segmentation. To train the network, a large set of image patches with corresponding road label information are applied. The final network is then applied to detect and map forest roads from lidar data covering the Etnedal municipality in Norway. The results show that we are able to map the forest roads with an overall accuracy of 97.2%. We conclude that the method has a strong potential for large-scale operational mapping of forest roads.

引用

页码：193 / 204

页数：12

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