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

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...

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Bibliographic Details
Published in:Scientific programming Vol. 2023; pp. 1 - 15
Main Authors: Norelyaqine, Abderrahim, Azmi, Rida, Saadane, Abderrahim
Format: Journal Article
Language:English
Published: New York Hindawi 2023
John Wiley & Sons, Inc
Subjects:
Accuracy
Algorithms
Buildings
Classification
Coders
Deep learning
Encoders-Decoders
Graph representations
High resolution
Image analysis
Image processing
Image resolution
Image segmentation
Machine learning
Medical imaging
Methods
Monitoring
Neural networks
Remote sensing
Semantic segmentation
Urban planning
Wavelet transforms
ISSN:1058-9244, 1875-919X
Online Access:Get full text
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Summary: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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ISSN:1058-9244
1875-919X
DOI:10.1155/2023/8552624