A Deeping Learning Based Framework and System for Effective Land Use Mapping

Liao, X; Huang, X; Huang, W

A Deeping Learning Based Framework and System for Effective Land Use Mapping

Liao, X Huang, X Huang, W

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Cooperative Design, Visualization, and Engineering, 2023, 14166 LNCS, pp. 174-180
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Liao, X
dc.contributor.author	Huang, X
dc.contributor.author	Huang, W https://orcid.org/0000-0002-5190-7839
dc.date	2023-10-01
dc.date.accessioned	2024-05-30T20:18:42Z
dc.date.available	2024-05-30T20:18:42Z
dc.date.issued	2023-01-01
dc.identifier.citation	Cooperative Design, Visualization, and Engineering, 2023, 14166 LNCS, pp. 174-180
dc.identifier.isbn	9783031438141
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/179355
dc.description.abstract	This paper proposes and implements a novel framework that uses deep learning to classify and visualise satellite land images. The proposed framework uses deep learning to accurately detect features in satellite images, automating the extraction of useful information from large datasets. It involves building and training a deep learning module using various algorithms and settings to improve geographic data processing. Overall, this paper contributes to the field of spatial image processing and highlights the potential benefits of deep learning in land-use mapping and related applications. The implementation of this technology can increase agricultural productivity, improve natural disaster management, and protect the environment.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Cooperative Design, Visualization, and Engineering
dc.relation.ispartof	International Conference on Cooperative Design, Visualization and Engineering
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-031-43815-8_17
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	A Deeping Learning Based Framework and System for Effective Land Use Mapping
dc.type	Conference Proceeding
utslib.citation.volume	14166 LNCS
utslib.location.activity	Mallorca, Spain
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
pubs.organisational-group	University of Technology Sydney/Provost/TD School
pubs.organisational-group	University of Technology Sydney/Strength - VI - Visualisation Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-05-30T20:18:41Z
pubs.finish-date	2023-10-04
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2023-10-01
pubs.volume	14166 LNCS
dc.location	Switzerland

Abstract:

This paper proposes and implements a novel framework that uses deep learning to classify and visualise satellite land images. The proposed framework uses deep learning to accurately detect features in satellite images, automating the extraction of useful information from large datasets. It involves building and training a deep learning module using various algorithms and settings to improve geographic data processing. Overall, this paper contributes to the field of spatial image processing and highlights the potential benefits of deep learning in land-use mapping and related applications. The implementation of this technology can increase agricultural productivity, improve natural disaster management, and protect the environment.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/179355