Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area Selection
2.2. Data Sources
2.3. Land Use Classification
2.3.1. Image and Training Dataset Preparation
2.3.2. Image Classification
2.3.3. Post-Classification Processing
2.4. Accuracy Assessment
2.5. Data Analysis
3. Results
3.1. Availability of Landsat Satellite Imagery
3.2. Availability of Historic Google Earth Imagery
3.3. Settlement Growth in Mining and Non-Mining Areas
3.4. Accuracy Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Landsat Mission | Bissa | Taparko | Essakane | Youga | Total |
---|---|---|---|---|---|
Landsat 5 | 25 (6) | 28 (13) | 27 (8) | 21 (6) | 101 (33) |
Landsat 7 | 133 (27) | 117 (35) | 95 (32) | 83 (32) | 428 (126) |
Landsat 8 | 46 (14) | 48 (13) | 53 (10) | 40 (15) | 187 (52) |
Total | 204 (47) | 193 (61) | 175 (50) | 144 (53) | 716 (211) |
Approach 1 | Classification | Approach 2 | Classification | ||||
Reference | Built-up | Other | PA | Reference | Built-up | Other | PA |
Built-up | 130 | 197 | 39.8% | Built-up | 438 | 99 | 81.6% |
Other | 27 | 462 | 94.5% | Other | 3 | 703 | 99.6% |
UA | 82.8% | 70.1% | UA | 95.0% | 87.7% | ||
OA = 72.5% | OA = 91.8% | ||||||
Kappa = 0.375 | Kappa = 0.829 | ||||||
Bissa | Classification | Taparko | Classification | ||||
Reference | Built-up | Other | PA | Reference | Built-up | Other | PA |
Built-up | 70 | 52 | 57.4% | Built-up | 60 | 145 | 29.3% |
Other | 4 | 285 | 98.6% | Other | 23 | 177 | 88.5% |
UA | 94.6% | 84.6% | UA | 72.3% | 55.0% | ||
OA = 86.4% | OA = 58.5% | ||||||
Kappa = 0.632 | Kappa = 0.176 | ||||||
Essakane | Classification | PA | Youga | Classification | PA | ||
Reference | Built-up | Other | Reference | Built-up | Other | ||
Built-up | 24 | 55 | 30.4% | Built-up | 414 | 44 | 90.4% |
Other | 0 | 200 | 100% | Other | 3 | 503 | 99.4% |
UA | 100% | 78.4% | UA | 99.3% | 92.0% | ||
OA = 80.3% | OA = 95.1% | ||||||
Kappa = 0.385 | Kappa = 0.902 |
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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
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, Andrea Farnham, Kees de Hoogh, and Mirko S. Winkler. 2020. "Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning" Remote Sensing 12, no. 2: 235. https://doi.org/10.3390/rs12020235
APA StyleDietler, D., Farnham, A., de Hoogh, K., & Winkler, M. S. (2020). Quantification of Annual Settlement Growth in Rural Mining Areas Using Machine Learning. Remote Sensing, 12(2), 235. https://doi.org/10.3390/rs12020235