Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals
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Description
The United Nations (UN) and World Bank have set Sustainable Development Goals (SDGs), with the aim for countries to reach targets related to important aspects of quality of life by 2030. An essential element of sustainable development is achieving social and economic aims to improve human quality of life, while conserving and managing natural resources. Earth observation data, such as satellite imagery data, are increasingly being used for monitoring the SDGs, and statistical machine learning methods are commonly used to analyse these types of data. However, current methods often exclude the spatial information inherent in earth observation data, which can provide useful insights. In this paper we review how spatial information is currently measured for remote sensing data, describe spatial machine learning methods in the literature and opportunities for further development of spatial methods. We also describe a minimum set of requirements to measure SDGs from satellite imagery data.
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ID Code: | 123263 | ||||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||||
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Measurements or Duration: | 14 pages | ||||||
Keywords: | big data, decision trees, machine learning, remote sensing, satellite images, spatial data, sustainable development goals | ||||||
Pure ID: | 33310570 | ||||||
Divisions: | Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS) |
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Copyright Owner: | CopyrightOwner{2018 [Please consult the author]}CopyrightOwner | ||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
Deposited On: | 29 Nov 2018 00:27 | ||||||
Last Modified: | 02 Mar 2024 02:29 |
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