Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts
Abstract
:1. Introduction
2. Materials and Methods
2.1. Satellite Image Data
2.2. Classification for Construction Area Detection
2.2.1. Mono-Temporal Classification Patterns
2.2.2. Multi-Temporal Approaches for Construction Area Detection
3. Results
3.1. Mono-Temporal Classification Results
3.2. Multi-Temporal Approaches
4. Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- RWI—Leibniz-Institut für Wirtschaftsforschung (Ed.) Big Data in der Makroökonomischen Analyse. Fachlos 3: Machbarkeitsstudie: Prognose von Ausrüstungsinvestitionen, Bauinvestitionen, Exporten mit Unkonventionellen Datenquellen und Methoden; Vorläufiger Endbericht: Essen, Germany, 2021; pp. 79–125. [Google Scholar]
- Rashed, T.; Jürgens, C. Remote Sensing of Urban and Suburban Areas, 1st ed.; Springer Book Series Remote Sensing and Digital Image Processing; Springer: London, UK, 2005; Volume 10. [Google Scholar] [CrossRef]
- Weng, Q.; Quattrochi, D. Urban Remote Sensing; CRC Press: Boca Raton, FL, USA, 2006. [Google Scholar] [CrossRef]
- Henits, L.; Jürgens, C.; Mucsi, L. Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data. Int. J. Remote Sens. 2016. [Google Scholar] [CrossRef]
- Li, H.; Wang, C.; Zhong, C.; Su, A.; Xiong, C.; Wang, J.; Liu, J. Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sens. 2017, 9, 249. [Google Scholar] [CrossRef] [Green Version]
- Shi, L.; Taubenböck, H.; Zhang, Z.; Liu, F.; Wurm, M. Urbanization in China from the end of 1980s until 2010—Spatial dynamics and patterns of growth using EO-data. Int. J. Digit. Earth 2019, 12, 78–94. [Google Scholar] [CrossRef]
- Ghazaryan, G.; Rienow, A.; Oldenburg, C.; Thonfeld, F.; Trampnau, B.; Sticksel, S.; Jürgens, C. Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach. Remote Sens. 2021, 13, 1694. [Google Scholar] [CrossRef]
- Durieux, L.; Lagabrielle, E.; Nelson, A. A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data. ISPRS J. Photogramm. Remote Sens. 2008, 63, 399–408. [Google Scholar] [CrossRef]
- Awrangjeb, M.; Hu, X.; Yang, B.; Tian, J. Editorial for Special Issue: “Remote Sensing based Building Extraction”. Remote Sens. 2020, 12, 549. [Google Scholar] [CrossRef] [Green Version]
- Hermosilla, T.; Ruiz, L.A.; Recio, J.A.; Estornell, J. Evaluation of automatic building detection approaches combining high resolution images and LiDAR data. Remote Sens. 2011, 3, 1188–1210. [Google Scholar] [CrossRef] [Green Version]
- Hermosilla, T.; Ruiz, L.A.; Recio, J.A.; Balsa-Barreiro, J. Land-use mapping of Valencia city area from aerial images and LiDAR data. In GEOProcessing 2012: The Fourth International Conference in Advanced Geographic Information Systems, Applications and Services; International Academy, Research, and Industry Association (IARIA): Wilmington, MA, USA, 2012; pp. 232–237. [Google Scholar]
- Yan, W.Y.; Shaker, A.; El-Ashmawy, N. Urban land cover classification using airborne LiDAR data: A review. Remote Sens. Environ. 2015, 158, 295–310. [Google Scholar] [CrossRef]
- Varol, B.; Özlem Yılmaz, E.; Maktav, D.; Bayburt, S.; Gürdal, S. Detection of illegal constructions in urban cities: Comparing LIDAR data and stereo KOMPSAT-3 images with development plans. Eur. J. Remote Sens. 2019, 52, 335–344. [Google Scholar] [CrossRef] [Green Version]
- Lunetta, R.; Johnson, D.; Lyon, J.; Crotwell, J. Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sens. Environ. 2004, 89, 444–454. [Google Scholar] [CrossRef]
- Blaschke, T. Towards a framework for change detection based on image objects. In Remote Sensing & GIS for Environmental Studies; Erasmi, S., Cyffka, B., Kappas, M., Eds.; Göttinger Geographische Abhandlungen: Göttingen, Germany, 2005; Volume 113, pp. 1–9. [Google Scholar]
- Martinez, L.; Pala, V.; Arbiol, R.; Pineda, L.; Joaniquet, M. Urban Change Detection on Satellite Images Series. Application to Catalunya Area. In Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France, 11–13 April 2007; pp. 1–5. [Google Scholar] [CrossRef] [Green Version]
- Taubenböck, H.; Esch, T.; Wurm, M.; Roth, A.; Dech, S. Object-based feature extraction using high spatial resolution satellite data of urban areas. J. Spat. Sci. 2010, 55, 117–133. [Google Scholar] [CrossRef]
- De Vecchi, D.; Galeazzo, D.A.; Harb, M.; Dell’Acqua, F. Unsupervised change detection for urban expansion monitoring: An object-based approach. In Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26–31 July 2015; pp. 350–352. [Google Scholar] [CrossRef]
- Ma, L.; Li, M.; Blaschke, T.; Ma, X.; Tiede, D.; Cheng, L.; Chen, Z.; Chen, D. Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods. Remote Sens. 2016, 8, 761. [Google Scholar] [CrossRef] [Green Version]
- Benedetti, A.; Picchiani, M.; Latini, D.; Del Frate, F.; Schiavon, G. COSMO-SkyMed for Unsupervised Urban Change Detection using Radar Backscattering and Interferometric Coherence. In Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 485–488. [Google Scholar] [CrossRef]
- Mao, W.; Lu, D.; Hou, L.; Liu, X.; Yue, W. Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sens. 2020, 12, 2817. [Google Scholar] [CrossRef]
- DigitalGlobe. 2020. Available online: http://www.digitalglobe.com/ (accessed on 10 September 2020).
25 April 2015 | 20 November 2016 | 29 May 2017 | 29 April 2018 | 30 October 2019 | 8 August 2020 | |
---|---|---|---|---|---|---|
Satellite | WV-3 | WV-3 | WV-4 | WV-3 | WV-2 | WV-2 |
Resolution | 0.3 m | 0.3 m | 0.3 m | 0.3 m | 0.5 m | 0.4 m |
Color | Class | Group * |
---|---|---|
Bare soil | unsealed | |
Vegetation | ||
Construction | ||
Industry | sealed | |
Residential | ||
Other Artificial | ||
Water |
Feature Group | Band/Attribute | Based on … |
---|---|---|
Spectral | Blue | Means, standard deviations, maximum differences |
Green | ||
Red | ||
Infrared | ||
Indices | NDVI | |
HSI-Transformation | Saturation, intensity | Red, green, blue |
A | Construction Area on Previously Unsealed Surfaces with Subsequent Sealing | |||||
2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
unsealed | construction | sealed | ||||
unsealed | construction | sealed | ||||
unsealed | construction | sealed | ||||
unsealed | construction | sealed | ||||
B | Biennial Construction Areas with Subsequent Sealing | |||||
2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
construction | sealed | |||||
construction | sealed | |||||
construction | sealed | |||||
construction | sealed |
km2 | 25 April 2015 | 20 November 2016 | 29 May 2017 | 29 April 2018 | 30 October 2019 | 8 August 2020 |
---|---|---|---|---|---|---|
Bare Soil | 1.40 | 7.19 | 4.14 | 3.16 | 2.68 | 7.82 |
Vegetation | 20.18 | 15.57 | 17.44 | 18.12 | 17.28 | 13.17 |
Construction | 0.27 | 0.39 | 0.76 | 1.06 | 0.14 | 1.29 |
Industry | 1.54 | 1.33 | 1.40 | 1.03 | 0.86 | 0.77 |
Residential | 2.50 | 0.78 | 3.11 | 1.85 | 4.61 | 3.43 |
Other Artificial | 3.74 | 4.37 | 2.78 | 4.38 | 4.06 | 3.14 |
Water | 0.02 | 0.02 | 0.02 | 0.04 | 0.02 | 0.02 |
Sum | 29.64 | 29.64 | 29.64 | 29.64 | 29.64 | 29.64 |
Approach | Total | True Positive | ||
---|---|---|---|---|
Mono-temporal Pattern 2015 | 266,821 m2 | 33,641 m2 | 12.6% | |
Mono-temporal Pattern 2016 | 389,695 m2 | 55,351 m2 | 14.2% | |
Mono-temporal Pattern 2017 | 757,578 m2 | 144,010 m2 | 19.0% | |
Mono-temporal Pattern 2018 | 1,061,411 m2 | 406,272 m2 | 38.2% | |
Mono-temporal Pattern 2019 | 141,134 m2 | 71,006 m2 | 50.3% | |
A | Multi-temporal 2015–2017 | 27,528 m2 | 26,821 m2 | 97.4% |
Multi-temporal 2016–2018 | 49,107 m2 | 44,583 m2 | 90.7% | |
Multi-temporal 2017–2019 | 118,761 m2 | 112,293 m2 | 94.5% | |
Multi-temporal 2018–2020 | 11,994 m2 | 10,670 m2 | 88.9% | |
B | Biennial 2015 & 2016 | 458 m2 | 77 m2 | 16.8% |
Biennial 2016 & 2017 | 10,083 m2 | 1364 m2 | 13.5% | |
Biennial 2017 & 2018 | 68,469 m2 | 28,340 m2 | 41.3% | |
Biennial 2018 & 2019 | 791 m2 | 620 m2 | 78.3% |
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Juergens, C.; Meyer-Heß, M.F. Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts. Remote Sens. 2021, 13, 2618. https://doi.org/10.3390/rs13132618
Juergens C, Meyer-Heß MF. Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts. Remote Sensing. 2021; 13(13):2618. https://doi.org/10.3390/rs13132618
Chicago/Turabian StyleJuergens, Carsten, and M. Fabian Meyer-Heß. 2021. "Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts" Remote Sensing 13, no. 13: 2618. https://doi.org/10.3390/rs13132618
APA StyleJuergens, C., & Meyer-Heß, M. F. (2021). Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts. Remote Sensing, 13(13), 2618. https://doi.org/10.3390/rs13132618