Pesek Ondrej, Krisztian Lina, Landa Martin, Metz Markus, Neteler Markus
Department of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, Prague, Czech Republic.
Mundialis GmbH & Co. KG, Bonn, North Rhine-Westphalia, Germany.
PeerJ Comput Sci. 2024 Dec 23;10:e2571. doi: 10.7717/peerj-cs.2571. eCollection 2024.
Any place the human species inhabits is inevitably modified by them. One of the first features that appear everywhere, in urban areas as well as in the countryside or deep forests, are roads. Further, roads and streets in general reflect their omnipresent and significant role in our lives through the flow of goods, people, and even culture and information. However, their contribution to the public is highly influenced by their surface. Yet, research on automated road surface classification from remotely sensed data is peculiarly scarce. This work investigates the capacities of chosen convolutional neural networks (fully convolutional network (FCN), U-Net, SegNet, DeepLabv3+) on this task. We find that convolutional neural network (CNN) are capable of distinguishing between compact (asphalt, concrete) and modular (paving stones, tiles) surfaces for both roads and sidewalks on aerial data of spatial resolution of 10 cm. U-Net proved its position as the best-performing model among the tested ones, reaching an overall accuracy of nearly 92%. Furthermore, we explore the influence of adding a near-infrared band to the basic red green blue (RGB) scenes and stress where it should be used and where avoided. Overfitting strategies such as dropout and data augmentation undergo the same examination and clearly show their pros and cons. Convolutional neural networks are also compared to single-pixel based random forests and show indisputable advantage of the context awareness in convolutional neural networks, U-Net reaching almost 25% higher accuracy than random forests. We conclude that convolutional neural networks and U-Net in particular should be considered as suitable approaches for automated semantic segmentation of road surfaces on aerial imagery, while common overfitting strategies should only be used under particular conditions.
人类居住的任何地方都会不可避免地被他们改变。无论是在城市地区、乡村还是深山老林，道路都是最先出现的特征之一。此外，道路和街道总体上通过货物、人员甚至文化和信息的流动，反映出它们在我们生活中无所不在且至关重要的作用。然而，它们对公众的贡献在很大程度上受到其路面状况的影响。然而，利用遥感数据进行自动化路面分类的研究却极为稀少。这项工作研究了所选卷积神经网络（全卷积网络（FCN）、U-Net、SegNet、深度卷积神经网络v3+）在这项任务中的能力。我们发现，卷积神经网络能够在空间分辨率为10厘米的航空数据上区分道路和人行道上的致密（沥青、混凝土）和模块化（铺路石、瓷砖）表面。U-Net在测试模型中证明了自己是表现最佳的模型，总体准确率接近92%。此外，我们探讨了在基本红绿蓝（RGB）场景中添加近红外波段的影响，并强调了其使用和避免使用的情况。诸如随机失活和数据增强等过拟合策略也进行了同样的检验，并清楚地显示了它们的优缺点。卷积神经网络还与基于单像素的随机森林进行了比较，显示出卷积神经网络在上下文感知方面具有无可争议的优势，U-Net的准确率比随机森林高出近%。我们得出结论，卷积神经网络，特别是U-Net，应被视为航空图像路面自动语义分割的合适方法而常见的过拟合策略仅应在特定条件下使用。
