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Building Extraction from RGB Satellite Images using Deep Learning: A U-Net Approach

Authors:

Anastasios Temenos,

Eftychios Protopapadakis,

Anastasios Doulamis,

Nikos TemenosAuthors Info & Claims

PETRA '21: Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference

Pages 391 - 395

https://doi.org/10.1145/3453892.3461320

Published: 29 June 2021 Publication History

Abstract

Automatic building extraction from satellite RGB images, is a low-cost alternative to perform important urban planning tasks. Yet, it is a challenging one, especially when natural and non-city block objects interfere in the semantic segmentation of algorithms that extract their key features. In this work we approach the automatic building extraction using a Convolution Neural Network based on the U-Net architecture. In contrast to existing approaches, it successfully encodes important features and decodes the buildings’ localization by requiring both reduced computational time and dataset size. We evaluate the U-Net’s performance using RGB images selected from the SpaceNet 1 dataset and the experimental results show an accuracy in building localization of 92.3%. Finally, favorable comparison with existing CNN approaches to hyperspectral images targeting the SpaceNet 1 dataset, demonstrated its effectiveness.

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Cited By

Singla JPatel HPatel D(2024)A Novel Deep Learning Approach for High-Resolution Satellite-Based DEM FilteringJournal of the Indian Society of Remote Sensing10.1007/s12524-024-01902-552:8(1675-1686)Online publication date: 15-Jun-2024
https://doi.org/10.1007/s12524-024-01902-5
Prabhakar DGarg P(2023)Applying a Deep Learning Approach for Building Extraction From High-Resolution Remote Sensing ImageryEmerging Trends, Techniques, and Applications in Geospatial Data Science10.4018/978-1-6684-7319-1.ch008(157-179)Online publication date: 7-Apr-2023
https://doi.org/10.4018/978-1-6684-7319-1.ch008
Niu Y(2023)Multi-featured multi-scale combination of high-resolution remote sensing images for building extractionApplied Mathematics and Nonlinear Sciences10.2478/amns.2023.1.000709:1Online publication date: 28-Apr-2023
https://doi.org/10.2478/amns.2023.1.00070
Show More Cited By

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cover image ACM Other conferences

PETRA '21: Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference

June 2021

593 pages

ISBN:9781450387927

DOI:10.1145/3453892

Conference Chair:
Fillia Makedon

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2021

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Horizon 2020 programme

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PETRA '21

PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference

June 29 - July 2, 2021

Corfu, Greece

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Cited By

Singla JPatel HPatel D(2024)A Novel Deep Learning Approach for High-Resolution Satellite-Based DEM FilteringJournal of the Indian Society of Remote Sensing10.1007/s12524-024-01902-552:8(1675-1686)Online publication date: 15-Jun-2024
https://doi.org/10.1007/s12524-024-01902-5
Prabhakar DGarg P(2023)Applying a Deep Learning Approach for Building Extraction From High-Resolution Remote Sensing ImageryEmerging Trends, Techniques, and Applications in Geospatial Data Science10.4018/978-1-6684-7319-1.ch008(157-179)Online publication date: 7-Apr-2023
https://doi.org/10.4018/978-1-6684-7319-1.ch008
Niu Y(2023)Multi-featured multi-scale combination of high-resolution remote sensing images for building extractionApplied Mathematics and Nonlinear Sciences10.2478/amns.2023.1.000709:1Online publication date: 28-Apr-2023
https://doi.org/10.2478/amns.2023.1.00070
Nsiah RMantey SZiggah Y(2023)Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithmSmart Construction and Sustainable Cities10.1007/s44268-023-00019-x1:1Online publication date: 12-Dec-2023
https://doi.org/10.1007/s44268-023-00019-x

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