干旱区生态环境知识资源中心(Arid): Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning

Arid

DOI	10.3390/rs12020235
	Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning
	Dietler, Dominik 1,2; Farnham, Andrea 1,2; de Hoogh, Kees 1,2; Winkler, Mirko S.1,2
通讯作者	Dietler, Dominik
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:2
英文摘要	Studies on annual settlement growth have mainly focused on larger cities or incorporated data rarely available in, or applicable to, sparsely populated areas in sub-Saharan Africa, such as aerial photography or night-time light data. The aim of the present study is to quantify settlement growth in rural communities in Burkina Faso affected by industrial mining, which often experience substantial in-migration. A multi-annual training dataset was created using historic Google Earth imagery. Support vector machine classifiers were fitted on Landsat scenes to produce annual land use classification maps. Post-classification steps included visual quality assessments, majority voting of scenes of the same year and temporal consistency correction. Overall accuracy in the four studied scenes ranged between 58.5% and 95.1%. Arid conditions and limited availability of Google Earth imagery negatively affected classification accuracy. Humid study sites, where training data could be generated in proximity to the areas of interest, showed the highest classification accuracies. Overall, by relying solely on freely and globally available imagery, the proposed methodology is a promising approach for tracking fast-paced population dynamics in rural areas where population data is scarce. With the growing availability of longitudinal high-resolution imagery, including data from the Sentinel satellites, the potential applications of the methodology presented will further increase in the future.
英文关键词	Landsat Google Earth rural settlement land use classification machine learning remote sensing mining migration
类型	Article
语种	英语
国家	Switzerland
开放获取类型	gold, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000515569800034
WOS关键词	ANNUAL URBAN-DYNAMICS ; TIME-SERIES ; LAND-COVER ; METROPOLITAN REGION ; PATTERNS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315409
作者单位	1.Swiss Trop & Publ Hlth Inst, CH-4002 Basel, Switzerland; 2.Univ Basel, CH-4003 Basel, Switzerland
推荐引用方式 GB/T 7714	Dietler, Dominik,Farnham, Andrea,de Hoogh, Kees,et al. Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning[J],2020,12(2).
APA	Dietler, Dominik,Farnham, Andrea,de Hoogh, Kees,&Winkler, Mirko S..(2020).Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning.REMOTE SENSING,12(2).
MLA	Dietler, Dominik,et al."Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning".REMOTE SENSING 12.2(2020).