Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning
1
Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland
2
University of Basel, P.O. Box, CH-4003 Basel, Switzerland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 235; https://doi.org/10.3390/rs12020235
Received: 6 December 2019 / Revised: 6 January 2020 / Accepted: 8 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Machine Learning of Remote Sensing Data for Urban Growth Analysis and Modeling)
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.
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Keywords:
Landsat; Google Earth; rural settlement; land use classification; machine learning; remote sensing; mining; migration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Dietler, D.; Farnham, A.; de Hoogh, K.; Winkler, M.S. Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning. Remote Sens. 2020, 12, 235. https://doi.org/10.3390/rs12020235
AMA Style
Dietler D, Farnham A, de Hoogh K, Winkler MS. Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning. Remote Sensing. 2020; 12(2):235. https://doi.org/10.3390/rs12020235
Chicago/Turabian StyleDietler, Dominik; Farnham, Andrea; de Hoogh, Kees; Winkler, Mirko S. 2020. "Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning" Remote Sens. 12, no. 2: 235. https://doi.org/10.3390/rs12020235
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