Automated Global Method to Detect Rapid and Future Urban Areas
As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict fut...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2073-445X/14/5/1061 |
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| author | Heather S. Sussman Sarah J. Becker |
| author_facet | Heather S. Sussman Sarah J. Becker |
| author_sort | Heather S. Sussman |
| collection | DOAJ |
| description | As many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict future urban areas often rely on deep learning algorithms, which can be computationally expensive and require a large data volume. Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. In this study, rapid and future urbanization algorithms are developed, which are based on methods that use an ensemble of built-up spectral indices and a random forest classifier to detect built-up land cover in Sentinel-2 imagery, across ten sites that vary in their climate and population. Results show that the rapid urbanization algorithm can highlight anomalous urban growth. The future urbanization algorithm had an average overall accuracy of 0.66 (±0.11) and an average F1-score of 0.46 (±0.23). However, the method performed well in areas without seasonal vegetation changes and bare ground surroundings with overall accuracy values and F1-scores near or over 0.80. Overall, these methods provide an automated global approach to identifying rapid and future urban areas with minimal data and computational resources needed, which can enable urban planners to obtain information quickly so that decision making for city planning can be completed faster. |
| format | Article |
| id | doaj-art-e27fc7df452c43f39d1dad96d04bf0ae |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-e27fc7df452c43f39d1dad96d04bf0ae2025-08-20T02:33:47ZengMDPI AGLand2073-445X2025-05-01145106110.3390/land14051061Automated Global Method to Detect Rapid and Future Urban AreasHeather S. Sussman0Sarah J. Becker1Geospatial Research Laboratory, Engineering Research and Development Center, US Army Corps of Engineers, Alexandria, VA 22315, USAGeospatial Research Laboratory, Engineering Research and Development Center, US Army Corps of Engineers, Alexandria, VA 22315, USAAs many areas of the world continue to grow, it is important to detect areas that are urbanizing at paces above the norm and predict future urban areas, so that optimal city planning can occur. However, methods to detect rapid urbanization are currently absent. Additionally, methods that predict future urban areas often rely on deep learning algorithms, which can be computationally expensive and require a large data volume. Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. In this study, rapid and future urbanization algorithms are developed, which are based on methods that use an ensemble of built-up spectral indices and a random forest classifier to detect built-up land cover in Sentinel-2 imagery, across ten sites that vary in their climate and population. Results show that the rapid urbanization algorithm can highlight anomalous urban growth. The future urbanization algorithm had an average overall accuracy of 0.66 (±0.11) and an average F1-score of 0.46 (±0.23). However, the method performed well in areas without seasonal vegetation changes and bare ground surroundings with overall accuracy values and F1-scores near or over 0.80. Overall, these methods provide an automated global approach to identifying rapid and future urban areas with minimal data and computational resources needed, which can enable urban planners to obtain information quickly so that decision making for city planning can be completed faster.https://www.mdpi.com/2073-445X/14/5/1061land cover predictionSentinel-2spectral indexurbanization |
| spellingShingle | Heather S. Sussman Sarah J. Becker Automated Global Method to Detect Rapid and Future Urban Areas Land land cover prediction Sentinel-2 spectral index urbanization |
| title | Automated Global Method to Detect Rapid and Future Urban Areas |
| title_full | Automated Global Method to Detect Rapid and Future Urban Areas |
| title_fullStr | Automated Global Method to Detect Rapid and Future Urban Areas |
| title_full_unstemmed | Automated Global Method to Detect Rapid and Future Urban Areas |
| title_short | Automated Global Method to Detect Rapid and Future Urban Areas |
| title_sort | automated global method to detect rapid and future urban areas |
| topic | land cover prediction Sentinel-2 spectral index urbanization |
| url | https://www.mdpi.com/2073-445X/14/5/1061 |
| work_keys_str_mv | AT heatherssussman automatedglobalmethodtodetectrapidandfutureurbanareas AT sarahjbecker automatedglobalmethodtodetectrapidandfutureurbanareas |