Deep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery

References

• Abdel-Hamid, A., O. Dubovyk, and K. Greve. 2021. “The Potential of Sentinel-1 InSAR Coherence for Grasslands Monitoring in Eastern Cape, South Africa.” International Journal of Applied Earth Observation and Geoinformation 98: 102306. doi:10.1016/j.jag.2021.102306.
   Web of Science ®Google Scholar
• Alsaaideh, B., R. Tateishi, D.X. Phong, N.T. Hoan, A. Al-Hanbali, and B. Xiulian. 2017. “New Urban Map of Eurasia Using MODIS and Multi-source Geospatial Data.” Geo-spatial Information Science 20 (1): 29–38. doi:10.1080/10095020.2017.1288418.
   Web of Science ®Google Scholar
• Angelliaume, S., P.C. Dubois-Fernandez, C.E. Jones, B. Holt, B. Minchew, E. Amri, and V. Miegebielle. 2018. “SAR Imagery for Detecting Sea Surface Slicks: Performance Assessment of Polarization-Dependent Parameters.” IEEE Transactions on Geoscience and Remote Sensing 56 (8): 4237–4257. doi:10.1109/TGRS.2018.2803216.
   Web of Science ®Google Scholar
• Bechtel, B., P.J. Alexander, C. Beck, J. Böhner, O. Brousse, J. Ching, M. Demuzere, et al. 2019. “Generating WUDAPT Level 0 Data – Current Status of Production and Evaluation.” Urban Climate 27: 24–45. doi:10.1016/j.uclim.2018.10.001.
   Web of Science ®Google Scholar
• Bechtel, B., P. Alexander, J. Böhner, J. Ching, O. Conrad, J. Feddema, G. Mills, L. See, and I. Stewart. 2015. “Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities.” ISPRS International Journal of Geo-Information 4 (1): 199–219. doi:10.3390/ijgi4010199.
   Web of Science ®Google Scholar
• Bechtel, B., L. See, G. Mills, and M. Foley. 2016. “Classification of Local Climate Zones Using SAR and Multispectral Data in an Arid Environment.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (7): 3097–3105. doi:10.1109/JSTARS.2016.2531420.
   Web of Science ®Google Scholar
• Cai, M., C. Ren, Y. Xu, W. Dai, and X.M. Wang. 2016. “Local Climate Zone Study for Sustainable Megacities Development by Using Improved WUDAPT Methodology – A Case Study in Guangzhou.” Procedia Environmental Sciences 36: 82–89. doi:10.1016/j.proenv.2016.09.017.
   Google Scholar
• Cai, M., C. Ren, Y. Xu, K.K.-L. Lau, and R. Wang. 2018. “Investigating the Relationship between Local Climate Zone and Land Surface Temperature Using an Improved WUDAPT Methodology – A Case Study of Yangtze River Delta, China.” Urban Climate 24: 485–502. doi:10.1016/j.uclim.2017.05.010.
   Web of Science ®Google Scholar
• Chakraborty, S., J. Phukan, M. Roy, and B.B. Chaudhuri. 2020. “Handling the Class Imbalance in Land-Cover Classification Using Bagging-Based Semisupervised Neural Approach.” IEEE Geoscience and Remote Sensing Letters 17 (9): 1493–1497. doi:10.1109/LGRS.2019.2949248.
   Web of Science ®Google Scholar
• Cheng, G., X. Xie, J. Han, L. Guo, and G.-S. Xia. 2020. “Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 3735–3756. doi:10.1109/JSTARS.2020.3005403.
   Web of Science ®Google Scholar
• Ching, J., G. Mills, B. Bechtel, L. See, J. Feddema, X. Wang, C. Ren, et al. 2018. “WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene.” Bulletin of the American Meteorological Society 99 (9): 1907–1924. doi:10.1175/bams-d-16-0236.1.
   Web of Science ®Google Scholar
• Danylo, O., L. See, B. Bechtel, D. Schepaschenko, and S. Fritz. 2016. “Contributing to WUDAPT: A Local Climate Zone Classification of Two Cities in Ukraine.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (5): 1841–1853. doi:10.1109/jstars.2016.2539977.
   Web of Science ®Google Scholar
• Demuzere, M., B. Bechtel, and G. Mills. 2019. “Global Transferability of Local Climate Zone Models.” Urban Climate 27: 46–63. doi:10.1016/j.uclim.2018.11.001.
   Web of Science ®Google Scholar
• Feng, P., Y. Lin, J. Guan, Y. Dong, G. He, Z. Xia, and H. Shi. 2019. “Embranchment CNN Based Local Climate Zone Classification Using SAR and Multispectral Remote Sensing Data.” In IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan. July 28 - August 2. doi:10.1109/IGARSS.2019.8898703.
   Google Scholar
• Hadeel, A., M. Jabbar, and X. Chen. 2009. “Application of Remote Sensing and GIS to the Study of Land Use/Cover Change and Urbanization Expansion in Basrah Province, Southern Iraq.” Geo-spatial Information Science 12 (2): 135–141. doi:10.1007/s11806-009-0244-7.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, America. June 27–30. doi:10.1109/CVPR.2016.90.
   Google Scholar
• Hu, J., P. Ghamisi, and X. Zhu. 2018. “Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification.” ISPRS International Journal of Geo-Information 7 (9): 379. doi:10.3390/ijgi7090379.
   Web of Science ®Google Scholar
• Hu, J., L. Shen, S. Albanie, G. Sun, and E. Wu. 2020. “Squeeze-and-Excitation Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (8): 2011–2023. doi:10.1109/TPAMI.2019.2913372.
   PubMed Web of Science ®Google Scholar
• Huang, X., A. Liu, and J. Li. 2021. “Mapping and Analyzing the Local Climate Zones in China’s 32 Major Cities Using Landsat Imagery Based on A Novel Convolutional Neural Network.” Geo-spatial Information Science 1–30. doi:10.1080/10095020.2021.1892459.
   Web of Science ®Google Scholar
• Huang, G., Z. Liu, L.V.D. Maaten, and K.Q. Weinberger. 2017. “Densely Connected Convolutional Networks.” In IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, America. July 21-26. doi:10.1109/CVPR.2017.243.
   Google Scholar
• Huang, B., and J. Wang. 2020. “Big Spatial Data for Urban and Environmental Sustainability.” Geo-spatial Information Science 23 (2): 125–140. doi:10.1080/10095020.2020.1754138.
   Web of Science ®Google Scholar
• Jiang, Z., Y. Chen, and L. Jing. 2006. “On Urban Heat Island of Beijing Based on Landsat TM Data.” Geo-spatial Information Science 9 (4): 293–297. doi:10.1007/BF02826743.
   Google Scholar
• Jing, H., Y. Feng, W. Zhang, Y. Zhang, S. Wang, K. Fu, and K. Chen. 2019. “Effective Classification of Local Climate Zones Based on Multi-Source Remote Sensing Data.” In IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan. July 28 - August 2. doi:10.1109/IGARSS.2019.8898475.
   Google Scholar
• Kajimoto, M., and J. Susaki. 2013. “Urban Density Estimation from Polarimetric SAR Images Based on a POA Correction Method.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (3): 1418–1429. doi:10.1109/JSTARS.2013.2255584.
   Web of Science ®Google Scholar
• Kim, M., D. Jeong, H. Choi, and Y. Kim. 2020. “Developing High Quality Training Samples for Deep Learning Based Local Climate Zone Classification in Korea.” arXiv preprint arXiv:2011.01436.
   Google Scholar
• La, Y., H. Bagan, and Y. Yamagata. 2020. “Urban Land Cover Mapping under the Local Climate Zone Scheme Using Sentinel-2 and PALSAR-2 Data.” Urban Climate 33: 100661. doi:10.1016/j.uclim.2020.100661.
   Web of Science ®Google Scholar
• Leconte, F., J. Bouyer, and R. Claverie. 2020. “Nocturnal Cooling in Local Climate Zone: Statistical Approach Using Mobile Measurements.” Urban Climate 33: 100629. doi:10.1016/j.uclim.2020.100629.
   Web of Science ®Google Scholar
• Lee, J.S., and E. Pottier. 2009. Polarimetric Radar Imaging: From Basics to Applications. Boca Raton, Fl, USA: CRC Press.
   Google Scholar
• Li, D., J. Ma, T. Cheng, J.L. van Genderen, and Z. Shao. 2019. “Challenges and Opportunities for the Development of Megacities.” International Journal of Digital Earth 12 (12): 1382–1395. doi:10.1080/17538947.2018.1512662.
   Web of Science ®Google Scholar
• Li, Y., and Y. Zhang. 2014. “Synthetic Aperture Radar Oil Spills Detection Based on Morphological Characteristics.” Geo-spatial Information Science 17 (1): 8–16. doi:10.1080/10095020.2014.883109.
   Google Scholar
• Li, D., X. Zhao, and X. Li. 2016. “Remote Sensing of Human Beings – A Perspective from Nighttime Light.” Geo-spatial Information Science 19 (1): 69–79. doi:10.1080/10095020.2016.1159389.
   Web of Science ®Google Scholar
• Lin, T.-Y., P. Goyal, R. Girshick, K. He, and P. Dollár. 2017. “Focal Loss for Dense Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (2): 318–327. doi:10.1109/TPAMI.2018.2858826.
   Google Scholar
• Liu, W., L. Chen, and Y. Chen. 2018. “Age Classification Using Convolutional Neural Networks with the Multi-class Focal Loss.” In IOP Conference Series: Materials Science and Engineering. Chengdu, China. July 19-22. doi:10.1088/1757-899X/428/1/012043.
   Google Scholar
• Liu, S., and Q. Shi. 2020. “Local Climate Zone Mapping as Remote Sensing Scene Classification Using Deep Learning: A Case Study of Metropolitan China.” ISPRS Journal of Photogrammetry and Remote Sensing 164: 229–242. doi:10.1016/j.isprsjprs.2020.04.008.
   Web of Science ®Google Scholar
• Maharoof, N., R. Emmanuel, and C. Thomson. 2020. “Compatibility of Local Climate Zone Parameters for Climate Sensitive Street Design: Influence of Openness and Surface Properties on Local Climate.” Urban Climate 33: 100642. doi:10.1016/j.uclim.2020.100642.
   Web of Science ®Google Scholar
• Mills, G., B. Bechtel, J. Ching, L. See, J. Feddema, M. Foley, P. Alexander, and M. O’Connor. 2015. “An Introduction to the WUDAPT Project.” In International Conference on Urban Climates. Toulouse, France. July 20- 24.
   Google Scholar
• Oke, T.R., and I.D. Stewart. 2012. “Local Climate Zones for Urban Temperature Studies.” Bulletin of the American Meteorological Society 93 (12): 1879–1900. doi:10.1175/bams-d-11-00019.1.
   Web of Science ®Google Scholar
• Oliveira, A., A. Lopes, and S. Niza. 2020. “Local Climate Zones in Five Southern European Cities: An Improved GIS-based Classification Method Based on Copernicus Data.” Urban Climate 33: 100631. doi:10.1016/j.uclim.2020.100631.
   Web of Science ®Google Scholar
• Qiu, C., X. Tong, M. Schmitt, B. Bechtel, and X.X. Zhu. 2020. “Multilevel Feature Fusion-Based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 2793–2806. doi:10.1109/jstars.2020.2995711.
   Web of Science ®Google Scholar
• Quan, S.J., and P. Bansal. 2021. “A Systematic Review of GIS-based Local Climate Zone Mapping Studies.” Building and Environment 196: 107791. doi:10.1016/j.buildenv.2021.107791.
   Web of Science ®Google Scholar
• Schmitt, M., L.H. Hughes, C. Qiu, and X.X. Zhu. 2019. “Aggregating Cloud-free Sentinel-2 Images with Google Earth Engine.” In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Munich, Germany. September 18- 20.
   Google Scholar
• Shao, Z., C. Li, D. Li, O. Altan, L. Zhang, and L. Ding. 2020. “An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land.” ISPRS International Journal of Geo-Information 9 (7): 448. doi:10.3390/ijgi9070448.
   Web of Science ®Google Scholar
• Shao, Z., N.S. Sumari, A. Portnov, F. Ujoh, W. Musakwa, and P.J. Mandela. 2021. “Urban Sprawl and Its Impact on Sustainable Urban Development: A Combination of Remote Sensing and Social Media Data.” Geo-spatial Information Science 24 (2): 241–255. doi:10.1080/10095020.2020.1787800.
   Web of Science ®Google Scholar
• Shao, Z., W. Wu, and S. Guo. 2020. “IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data.” Remote Sensing 12 (17): 2796. doi:10.3390/rs12172796.
   Web of Science ®Google Scholar
• Shen, P., L. Ouyang, C. Wang, Y. Shi, and Y. Su. 2020. “Cluster and Characteristic Analysis of Shanghai Metro Stations Based on Metro Card and Land-Use Data.” Geo-spatial Information Science 23 (4): 352–361. doi:10.1080/10095020.2020.1846463.
   Web of Science ®Google Scholar
• Shi, Y., C. Ren, K.K.-L. Lau, and E. Ng. 2019. “Investigating the Influence of Urban Land Use and Landscape Pattern on PM2.5 Spatial Variation Using Mobile Monitoring and WUDAPT.” Landscape and Urban Planning 189: 15–26. doi:10.1016/j.landurbplan.2019.04.004.
   Web of Science ®Google Scholar
• Shi, Y., C. Ren, M. Luo, J. Ching, and Z. Ren. 2021. “Utilizing World Urban Database and Access Portal Tools (WUDAPT) and Machine Learning to Facilitate Spatial Estimation of Heatwave Patterns.” Urban Climate 36: 100797. doi:10.1016/j.uclim.2021.100797.
   Web of Science ®Google Scholar
• Touzi, R., W.M. Boerner, and J. Lueneburg. 2004. “A Review of Polarimetry in the Context of Synthetic Aperture Radar: Concepts and Information Extraction.” Canadian Journal of Remote Sensing 30 (3): 380–407. doi:10.5589/m04-013.
   Web of Science ®Google Scholar
• Trinder, J., and Q. Liu. 2020. “Assessing Environmental Impacts of Urban Growth Using Remote Sensing.” Geo-spatial Information Science 23 (1): 20–39. doi:10.1080/10095020.2019.1710438.
   Web of Science ®Google Scholar
• Wang, X., R.B. Girshick, A. Gupta, and K. He. 2018. “Non-local Neural Networks.” In IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, America. June 18–23.
   Google Scholar
• Wu, H., Z. Gui, and Z. Yang. 2020. “Geospatial Big Data for Urban Planning and Urban Management.” Geo-spatial Information Science 23 (4): 273–274. doi:10.1080/10095020.2020.1854981.
   Web of Science ®Google Scholar
• Wu, J., Z. Zhao, C. Sun, R. Yan, and X. Chen. 2020. “Ss-InfoGAN for Class-Imbalance Classification of Bearing Faults.” Procedia Manufacturing 49: 99–104. doi:10.1016/j.promfg.2020.07.003.
   Google Scholar
• Yamaguchi, Y., Y. Yajima, and H. Yamada. 2006. “A Four-Component Decomposition of POLSAR Images Based on the Coherency Matrix.” IEEE Geoscience and Remote Sensing Letters 3 (3): 292–296. doi:10.1109/LGRS.2006.869986.
   Web of Science ®Google Scholar
• Yang, J., S. Jin, X. Xiao, C. Jin, J. Xia, X. Li, and S. Wang. 2019. “Local Climate Zone Ventilation and Urban Land Surface Temperatures: Towards A Performance-Based and Wind-Sensitive Planning Proposal in Megacities.” Sustainable Cities and Society 47: 101487. doi:10.1016/j.scs.2019.101487.
   Web of Science ®Google Scholar
• Yang, C., Q. Zhan, S. Gao, and H. Liu. 2020. “Characterizing the Spatial and Temporal Variation of the Land Surface Temperature Hotspots in Wuhan from A Local Scale.” Geo-spatial Information Science 23 (4): 327–340. doi:10.1080/10095020.2020.1834882.
   Web of Science ®Google Scholar
• Yoo, C., D. Han, J. Im, and B. Bechtel. 2019. “Comparison between Convolutional Neural Networks and Random Forest for Local Climate Zone Classification in Mega Urban Areas Using Landsat Images.” ISPRS Journal of Photogrammetry and Remote Sensing 157: 155–170. doi:10.1016/j.isprsjprs.2019.09.009.
   Web of Science ®Google Scholar
• Zeng, Y., W. Huang, F. Zhan, H. Zhang, and H. Liu. 2010. “Study on the Urban Heat Island Effects and Its Relationship with Surface Biophysical Characteristics Using MODIS Imageries.” Geo-spatial Information Science 13 (1): 1–7. doi:10.1007/s11806-010-0204-2.
   Google Scholar
• Zhang, J., M. Xing, G.-C. Sun, J. Chen, M. Li, Y. Hu, and Z. Bao. 2021. “Water Body Detection in High-Resolution SAR Images with Cascaded Fully-Convolutional Network and Variable Focal Loss.” IEEE Transactions on Geoscience and Remote Sensing 59 (1): 316–332. doi:10.1109/TGRS.2020.2999405.
   Web of Science ®Google Scholar
• Zhao, Z., L. Shen, L. Li, H. Wang, and B.-J. He. 2020. “Local Climate Zone Classification Scheme Can Also Indicate Local-Scale Urban Ventilation Performance: An Evidence-Based Study.” Atmosphere 11 (8): 776. doi:10.3390/atmos11080776.
   Web of Science ®Google Scholar
• Zheng, Y., C. Ren, Y. Xu, R. Wang, J. Ho, K. Lau, and E. Ng. 2018. “GIS-Based Mapping of Local Climate Zone in the High-Density City of Hong Kong.” Urban Climate 24: 419–448. doi:10.1016/j.uclim.2017.05.008.
   Web of Science ®Google Scholar
• Zhou, X., T. Okaze, C. Ren, M. Cai, Y. Ishida, and A. Mochida. 2020a. “Mapping Local Climate Zones for a Japanese Large City by an Extended Workflow of WUDAPT Level 0 Method.” Urban Climate 33: 100660. doi:10.1016/j.uclim.2020.100660.
   Web of Science ®Google Scholar
• Zhou, X., T. Okaze, C. Ren, M. Cai, Y. Ishida, H. Watanabe, and A. Mochida. 2020b. “Evaluation of Urban Heat Islands Using Local Climate Zones and the Influence of Sea-Land Breeze.” Sustainable Cities and Society 55: 102060. doi:10.1016/j.scs.2020.102060.
   Web of Science ®Google Scholar
• Zhou, Q., M. Zhai, and W. Yu. 2020. “Exploring Point Zero: A Study of 20 Chinese Cities.” Geo-spatial Information Science 23 (3): 258–272. doi:10.1080/10095020.2020.1779011.
   Web of Science ®Google Scholar
• Zhu, X., R. Huang, L.H. Hughes, H. Li, and Y. Hua. 2020. “So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification.” IEEE Geoscience and Remote Sensing Magazine 8 (3): 76–89. doi:10.1109/MGRS.2020.2964708.
   Google Scholar