SENTINEL-2 IMAGERY FOR MAPPING AND MONITORING IMPERVIOUSNESS IN URBAN AREAS

25 citations as recorded by crossref.

1. What would be better for urban mapping in the Klang Valley? SPOT or Sentinel-1 M. Wahab et al. 10.1088/1755-1315/1064/1/012021
2. Review of Experience in Recent Studies on the Dynamics of Land Urbanisation A. Biłozor & I. Cieślak 10.3390/land10111117
3. Characterization of water hyacinth ( Eichhornia crassipes (Mart.) Solms) biomass in Lake Tana, Ethiopia Y. Damtie et al. 10.1080/26895293.2022.2134933
4. Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification C. Ramezan 10.3390/rs14246218
5. Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment K. Onačillová et al. 10.3390/rs14164076
6. Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier B. Shrestha et al. 10.3390/rs13153040
7. Urban Big Data Analytics: A Novel Approach for Tracking Urbanization Trends in Sri Lanka N. Akalanka et al. 10.3390/land13060888
8. An Analysis of Urbanisation Dynamics with the Use of the Fuzzy Set Theory—A Case Study of the City of Olsztyn A. Biłozor et al. 10.3390/rs12111784
9. The Use of Geographic Databases for Analyzing Changes in Land Cover—A Case Study of the Region of Warmia and Mazury in Poland I. Cieślak et al. 10.3390/ijgi9060358
10. Country-wide assessment of biodiversity, naturalness and old-growth status using national forest inventory data C. Borghi et al. 10.1007/s10342-023-01620-6
11. Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications A. Nigar et al. 10.3389/fenvs.2024.1378443
12. A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery C. Ayala et al. 10.3390/rs13163135
13. Neural Network Fusion Processing and Inverse Mapping to Combine Multisensor Satellite Data and Analyze the Prominent Features G. Joshi et al. 10.1109/JSTARS.2023.3247788
14. Mapping the heat stress vulnerability landscape of Agartala City, India R. Neog 10.1007/s10708-024-11072-z
15. Identification and Location of a Transitional Zone between an Urban and a Rural Area Using Fuzzy Set Theory, CLC, and HRL Data A. Biłozor et al. 10.3390/su11247014
16. Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network D. Kim et al. 10.3390/w13111551
17. Digital Mapping of Land Cover Changes Using the Fusion of SAR and MSI Satellite Data G. Metrikaityte et al. 10.3390/land11071023
18. Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam N. Quang et al. 10.3390/su15021410
19. Uydu Görüntüleri Kullanarak Makine Öğrenme Yöntemleri ile Arazi Kullanımı ve Arazi Örtüsünün Belirlenmesi S. Oturanç 10.35234/fumbd.1469767
20. City Boundaries—Utilizing Fuzzy Set Theory for the Identification and Localization of the Urban–Rural Transition Zone A. Biłozor et al. 10.3390/su16219490
21. Understanding and Assessing Climate Change Risk to Green Infrastructure: Experiences from Greater Manchester (UK) J. Carter et al. 10.3390/land13050697
22. Kentsel Alanlarda Arazi Kullanım ve Arazi Örtüsü Belirleme Çalışmalarında Spektral İndekslerin Kullanılması: Ceyhan İlçe Merkezi Örneği S. Çiçekli 10.55213/kmujens.1446772
23. Ensemble Learning for Urban Flood Segmentation Through the Fusion of Multi-Spectral Satellite Data with Water Spectral Indices Using Row-Wise Cross Attention H. Xu & A. Woodley 10.3390/rs17010090
24. Kudzu invasion and its influential factors in the southeastern United States M. Shen et al. 10.1016/j.jag.2024.103872
25. Fusion of Sentinel-1 and Sentinel-2 data in mapping the impervious surfaces at city scale B. Shrestha et al. 10.1007/s10661-021-09321-6