ML-LUM: A system for land use mapping by machine learning algorithms

Liao, X; Huang, X; Huang, W

ML-LUM: A system for land use mapping by machine learning algorithms

Liao, X Huang, X Huang, W

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Publication Type:: Journal Article
Citation:: Journal of Computer Languages, 2019, 54
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Liao, X	en_US
dc.contributor.author	Huang, X	en_US
dc.contributor.author	Huang, W https://orcid.org/0000-0002-5190-7839	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	Journal of Computer Languages, 2019, 54	en_US
dc.identifier.issn	2665-9182	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136784
dc.description.abstract	© 2019 The land use mapping refers to mapping and assessing changes and patterns of land use. The use of agricultural land maps becomes increasingly important. The governments, private sectors, research agencies, and community groups rely on land use mapping data for natural resource assessment, monitoring, and planning. Finding an effective mapping approach is thereby crucial for natural resource condition monitoring and investment, agricultural productivity, sustainability and planning, biodiversity conservation, natural disaster management, and bio-security. In this paper, four machine learning algorithms, i.e., the classic k-Nearest Neighbour (kNN), Support Vector Machines (SVMs), Convolutional Neural Network (CNN), and newly developed Capsule Network (CapsNet), are applied to classify satellite images for land use. For comprehensively comparing the performance of different algorithms for land use mapping, the experiments have been conducted on real-world datasets. Based on the experiment results, several improvements on the algorithms are proposed in order to fulfil the requirement of a large-scale land mapping. In addition, we design and implement these algorithms for land use mapping in a Machine Learning Land Use Mapping (ML-LUM) system. The system is able to train the models, predict classifications of satellite images, map the land use, display the land use statistic data, and predict production yields. With a friendly graphic user interface for farmers, the system is implemented by using the cloud computing technique for processing large land use data. Furthermore, we present a case study. For the case study, a banana plantation area from a given satellite image is correctly marked and the area size is then calculated, together with predicting banana production.	en_US
dc.relation.ispartof	Journal of Computer Languages	en_US
dc.relation.isbasedon	10.1016/j.cola.2019.100908	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	ML-LUM: A system for land use mapping by machine learning algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	54	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

© 2019 The land use mapping refers to mapping and assessing changes and patterns of land use. The use of agricultural land maps becomes increasingly important. The governments, private sectors, research agencies, and community groups rely on land use mapping data for natural resource assessment, monitoring, and planning. Finding an effective mapping approach is thereby crucial for natural resource condition monitoring and investment, agricultural productivity, sustainability and planning, biodiversity conservation, natural disaster management, and bio-security. In this paper, four machine learning algorithms, i.e., the classic k-Nearest Neighbour (kNN), Support Vector Machines (SVMs), Convolutional Neural Network (CNN), and newly developed Capsule Network (CapsNet), are applied to classify satellite images for land use. For comprehensively comparing the performance of different algorithms for land use mapping, the experiments have been conducted on real-world datasets. Based on the experiment results, several improvements on the algorithms are proposed in order to fulfil the requirement of a large-scale land mapping. In addition, we design and implement these algorithms for land use mapping in a Machine Learning Land Use Mapping (ML-LUM) system. The system is able to train the models, predict classifications of satellite images, map the land use, display the land use statistic data, and predict production yields. With a friendly graphic user interface for farmers, the system is implemented by using the cloud computing technique for processing large land use data. Furthermore, we present a case study. For the case study, a banana plantation area from a given satellite image is correctly marked and the area size is then calculated, together with predicting banana production.

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http://hdl.handle.net/10453/136784