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

36 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. https://doi.org/10.1088/1755-1315/1064/1/012021
2. Review of Experience in Recent Studies on the Dynamics of Land Urbanisation A. Biłozor & I. Cieślak https://doi.org/10.3390/land10111117
3. A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data S. He et al. https://doi.org/10.1080/10106049.2025.2521833
4. Characterization of water hyacinth ( Eichhornia crassipes (Mart.) Solms) biomass in Lake Tana, Ethiopia Y. Damtie et al. https://doi.org/10.1080/26895293.2022.2134933
5. Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification C. Ramezan https://doi.org/10.3390/rs14246218
6. 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. https://doi.org/10.3390/rs14164076
7. Uso do sensoriamento remoto na análise da superfície impermeabilizada e da cobertura vegetal em Porto Alegre/RS G. Camargo et al. https://doi.org/10.5902/2236499492838
8. Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier B. Shrestha et al. https://doi.org/10.3390/rs13153040
9. Radiometric Cross-Calibration and Validation of KOMPSAT-3/AEISS Using Sentinel-2A/MSI J. Choi et al. https://doi.org/10.3390/rs17193280
10. Urban Big Data Analytics: A Novel Approach for Tracking Urbanization Trends in Sri Lanka N. Akalanka et al. https://doi.org/10.3390/land13060888
11. 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. https://doi.org/10.3390/rs12111784
12. A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al. https://doi.org/10.5194/acp-25-4035-2025
13. 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. https://doi.org/10.3390/ijgi9060358
14. ReSCD-Net: Resolution-Aware Deep Learning for Building Change Detection in High-Resolution Satellite Imagery M. Elisy et al. https://doi.org/10.1109/TGRS.2026.3662471
15. Country-wide assessment of biodiversity, naturalness and old-growth status using national forest inventory data C. Borghi et al. https://doi.org/10.1007/s10342-023-01620-6
16. Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications A. Nigar et al. https://doi.org/10.3389/fenvs.2024.1378443
17. Impact of urban habitat structure and weather variables on reproduction of gigant African snail <em>(Lissachatina fulica)</em> M. Garces-Restrepo et al. https://doi.org/10.31893/jabb.2025013
18. A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery C. Ayala et al. https://doi.org/10.3390/rs13163135
19. Evaluating Small-Scale Urban Regeneration Using Nighttime Lights and Sentinel-2: Evidence from Republic of Korea D. Jin & S. Choi https://doi.org/10.3390/urbansci10010036
20. Neural Network Fusion Processing and Inverse Mapping to Combine Multisensor Satellite Data and Analyze the Prominent Features G. Joshi et al. https://doi.org/10.1109/JSTARS.2023.3247788
21. Mapping the heat stress vulnerability landscape of Agartala City, India R. Neog https://doi.org/10.1007/s10708-024-11072-z
22. 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. https://doi.org/10.3390/su11247014
23. Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network D. Kim et al. https://doi.org/10.3390/w13111551
24. Digital Mapping of Land Cover Changes Using the Fusion of SAR and MSI Satellite Data G. Metrikaityte et al. https://doi.org/10.3390/land11071023
25. Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam N. Quang et al. https://doi.org/10.3390/su15021410
26. BİTKİ ÖRTÜSÜ İNDEKSLERİ KULLANILARAK BİTKİ ÖRTÜSÜ YOĞUNLUKLARININ ARAŞTIRILMASI: KARATEPE-ASLANTAŞ MİLLİ PARKI ÖRNEĞİ S. Oturanç https://doi.org/10.17780/ksujes.1521564
27. Investigating sensitivity to extreme weather of a mid-size global south city: an integrated MCDA (AHP)-GIS based modeling approach N. Ankhi & A. Khan https://doi.org/10.1007/s11069-025-07546-z
28. Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake J. Wang et al. https://doi.org/10.1080/20964471.2025.2597590
29. Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls D. Gagakuma https://doi.org/10.1007/s44327-025-00105-y
30. Uydu Görüntüleri Kullanarak Makine Öğrenme Yöntemleri ile Arazi Kullanımı ve Arazi Örtüsünün Belirlenmesi S. Oturanç https://doi.org/10.35234/fumbd.1469767
31. City Boundaries—Utilizing Fuzzy Set Theory for the Identification and Localization of the Urban–Rural Transition Zone A. Biłozor et al. https://doi.org/10.3390/su16219490
32. Understanding and Assessing Climate Change Risk to Green Infrastructure: Experiences from Greater Manchester (UK) J. Carter et al. https://doi.org/10.3390/land13050697
33. Kentsel Alanlarda Arazi Kullanım ve Arazi Örtüsü Belirleme Çalışmalarında Spektral İndekslerin Kullanılması: Ceyhan İlçe Merkezi Örneği S. Çiçekli https://doi.org/10.55213/kmujens.1446772
34. 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 https://doi.org/10.3390/rs17010090
35. Kudzu invasion and its influential factors in the southeastern United States M. Shen et al. https://doi.org/10.1016/j.jag.2024.103872
36. Fusion of Sentinel-1 and Sentinel-2 data in mapping the impervious surfaces at city scale B. Shrestha et al. https://doi.org/10.1007/s10661-021-09321-6