Deep Convolution Neural Network for Automated Method of Road Extraction on Aerial Imagery

Abderrahim, Norelyaqine; Saadane, Abderrahim; Azmi, Rida

doi:10.1007/978-3-030-80458-9_3

Cited by 1 publication

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“…Te latest DL algorithms have achieved excellent results in various applications, including natural language processing (NLP), visual data processing, and audio and voice processing. Convolutional neural networks (CNNs) with more hidden layers have a more complicated network structure and can learn and express features more efectively than classic machine learning approaches [11,12]. In remote sensing, the use of CNN has become crucial with the appearance of multispectral data at a very high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation

Abderrahim¹,

Azmi

Saadane³

2023

Scientific Programming

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Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing approaches are insufficient for autonomous building extraction from high-resolution aerial imaging applications. Automatic object extraction from high-resolution images has been achieved using semantic segmentation and deep learning models, which have become increasingly important in recent years. In this study, the U-Net model was used for building extraction, initially designed for biomedical image analysis. The encoder part of the U-Net model has been improved with ResNet50, VGG19, VGG16, DenseNet169, and Xception. However, three other models have been implemented to test the performance of the model studied: PSPNet, FPN, and LinkNet. The performance analysis through the intersection of union method has shown that U-Net with the VGG16 encoder presents the best results compared to the other models with a high IoU score of 83.06%. This research aims to examine the effectiveness of these four approaches for extracting buildings from high-resolution aerial data.

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Section: Introductionmentioning

confidence: 99%