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Research Article

National-scale mapping of building footprints using feature super-resolution semantic segmentation of Sentinel-2 images

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Article: 2196154 | Received 19 Jul 2022, Accepted 23 Mar 2023, Published online: 06 Apr 2023

References

  • Abadal, S., L. Salgueiro, J. Marcello, and V. Vilaplana. 2021. “A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery.” Remote Sensing 13 (22): 4547. doi:10.3390/rs13224547.
  • Ayala, C., C. Aranda, and M. Galar. 2021. “Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing.” Applied Sciences 11 (18): 8340. doi:10.3390/app11188340.
  • Balk, D. L., U. Deichmann, G. Yetman, F. Pozzi, S. I. Hay, and A. Nelson. 2006. “Determining Global Population Distribution: Methods, Applications and Data.” Advances in Parasitology 119–22. doi:10.1016/s0065-308x(05)62004-0.
  • Bartholomé, E., and A. S. Belward. 2005. “GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data.” International Journal of Remote Sensing 26 (9): 1959–1977. doi:10.1080/01431160412331291297.
  • Benjamin, S., H. L. Y. Melanie Laverdiere, A. Rose, and A. Rose. 2022. “Iterative Self-Organizing SCEne-LEvel Sampling (ISOSCELES) for Large-Scale Building Extraction.” GIScience & Remote Sensing 59 (1): 1–16. doi:10.1080/15481603.2021.2006433.
  • Bicheron, P., V. Amberg, L. Bourg, D. Petit, M. Huc, Miras, B., Brockmann, C., et al. 2011 . “Geolocation Assessment of MERIS GlobCover Orthorectified Products.” Geoscience and Remote Sensing, IEEE Transactions on 49 2972–2982 doi:10.1109/TGRS.2011.2122337 .
  • Brown, C. F., S. P. Brumby, B. Guzder-Williams, T. Birch, S. Brooks Hyde, J. Mazzariello, W. Czerwinski, et al. 2022. “Dynamic World, Near Real-Time Global 10 m Land Use Land Cover Mapping.” Scientific Data 9 (1): 251. doi:10.1038/s41597-022-01307-4.
  • Chen, J., J. Chen, A. Liao, X. Cao, L. Chen, X. Chen, H. Chaoying, et al. 2015. “Global Land Cover Mapping at 30m Resolution: A POK-Based Operational Approach.” ISPRS Journal of Photogrammetry and Remote Sensing 103 (May): 7–27. doi:10.1016/j.isprsjprs.2014.09.002.
  • Chen, J., P. Gong, H. Chunyang, W. Luo, M. Tamura, and P. Shi. 2002. “Assessment of the Urban Development Plan of Beijing by Using a CA-Based Urban Growth Model.” Photogrammetric Engineering and Remote Sensing 68: 1063–1072.
  • Chen, L.C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2018. “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–848. doi:10.1109/TPAMI.2017.2699184.
  • Corbane, C., V. Syrris, F. Sabo, P. Politis, M. Melchiorri, M. Pesaresi, P. Soille, and T. Kemper. 2021. “Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery.” Neural Computing & Applications 33 (12): 6697–6720. doi:10.1007/s00521-020-05449-7.
  • Dixit, Mayank, Kuldeep Chaurasia, and Vipul Kumar Mishra. 2021. “Dilated-ResUnet: A Novel Deep Learning Architecture for Building Extraction from Medium Resolution Multi-Spectral Satellite Imagery.” Expert Systems with Applications 184 (December): 115530. doi:10.1016/j.eswa.2021.115530.
  • Dong, C., C. Change Loy, H. Kaiming, and X. Tang. 2014. “Learning a Deep Convolutional Network for Image Super-Resolution.” In European Conference on Computer Vision, Zurich, Switzerland. September 6-12, 2014; 8692: 184–199. Springer, Cham. doi:10.1007/978-3-319-10593-2_13.
  • Elvidge, C. D., M. L. Imhoff, K. E. Baugh, V. Ruth Hobson, I. Nelson, J. Safran, J. B. Dietz, and B. T. Tuttle. 2001. “Night-Time Lights of the World: 1994–1995.” ISPRS Journal of Photogrammetry and Remote Sensing 56 (2): 81–99. doi:10.1016/S0924-2716(01)00040-5.
  • Elvidge, C., B. Tuttle, P. Sutton, K. Baugh, A. Howard, C. Milesi, B. Bhaduri, and R. Nemani. 2007. “Global Distribution and Density of Constructed Impervious Surfaces.” Sensors 7 (9): 1962–1979. doi:10.3390/s7091962.
  • Esch, T., M. Marconcini, A. Felbier, A. Roth, W. Heldens, M. Huber, M. Schwinger, H. Taubenbock, A. Muller, and S. Dech. 2013. “Urban Footprint Processor—fully Automated Processing Chain Generating Settlement Masks from Global Data of the TanDEM-X Mission.” IEEE Geoscience and Remote Sensing Letters 10 (6): 1617–1621. doi:10.1109/LGRS.2013.2272953.
  • Friedl, M. A., D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, et al. 2002. “Global Land Cover Mapping from MODIS: Algorithms and Early Results.” Remote Sensing of Environment 83 (1–2): 287–302. doi:10.1016/S0034-4257(02)00078-0.
  • Frolking, Steve, Tom Milliman, Karen C Seto, and Mark A Friedl. 2013. “A Global Fingerprint of Macro-Scale Changes in Urban Structure from 1999 to 2009.” Environmental Research Letters 8 (2): 024004. doi:10.1088/1748-9326/8/2/024004.
  • Gong, P., H. Liu, M. Zhang, L. Congcong, J. Wang, H. Huang, N. Clinton, et al. 2019. “Stable Classification with Limited Sample: Transferring a 30-M Resolution Sample Set Collected in 2015 to Mapping 10-M Resolution Global Land Cover in 2017.” Science Bulletin 64 (6): 370–373. doi:10.1016/j.scib.2019.03.002.
  • Gong, P., J. Wang, Y. Le, Y. Zhao, Y. Zhao, L. Liang, Z. Niu, et al. 2013. “Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data.” International Journal of Remote Sensing 34 (7): 2607–2654. doi:10.1080/01431161.2012.748992.
  • Guangming, W., Z. Guo, X. Shi, Q. Chen, X. Yongwei, R. Shibasaki, and X. Shao. 2018. “A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction.” Remote Sensing 10 (8): 1195. doi:10.3390/rs10081195.
  • Guo, Z., W. Guangming, X. Song, W. Yuan, Q. Chen, H. Zhang, X. Shi, et al. 2019. “Super-Resolution Integrated Building Semantic Segmentation for Multi-Source Remote Sensing Imagery.” IEEE Access 7: 99381–99397. doi:10.1109/ACCESS.2019.2928646.
  • Hansen, M. C., R. S. DeFries, J. R. Townshend, and R. Sohlberg. 2000. “Global Land Cover Classification at 1 Km Spatial Resolution Using a Classification Tree Approach.” International Journal of Remote Sensing 21 (6–7): 1331–1364. doi:10.1080/014311600210209.
  • Hasan, N., M. Shukor, and A. J. Ghandour. 2023. “Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery.” Signal, Image and Video Processing, 1–9. doi:10.1007/s11760-023-02520-3.
  • Heipke, C. 2010. “Crowdsourcing Geospatial Data.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (6): 550–557. doi:10.1016/j.isprsjprs.2010.06.005.
  • Homer, C., C. Huang, L. Yang, B. Wylie, and M. Coan. 2004. “Development of a 2001 National Land-Cover Database for the United States.” Photogrammetric Engineering & Remote Sensing 70 (7): 829–840. doi:10.14358/PERS.70.7.829.
  • Huang, X., D. Wen, L. Jiayi, and R. Qin. 2017. “Multi-Level Monitoring of Subtle Urban Changes for the Megacities of China Using High-Resolution Multi-View Satellite Imagery.” Remote Sensing of Environment 196 (July): 56–75. doi:10.1016/j.rse.2017.05.001.
  • Jiayi, L., X. Huang, T. Lilin, T. Zhang, and L. Wang. 2022. “A Review of Building Detection from Very High Resolution Optical Remote Sensing Images.” GIScience & Remote Sensing 59 (1): 1199–1225. doi:10.1080/15481603.2022.2101727.
  • Karra, K., C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis, and S. P. Brumby. 2021. “Global Land Use/Land Cover with Sentinel 2 and Deep Learning.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–4707. IEEE. 10.1109/IGARSS47720.2021.9553499.
  • Khoshboresh-Masouleh, M., and R. Shah-Hosseini. 2021. “Building Panoptic Change Segmentation with the Use of Uncertainty Estimation in Squeeze-And-Attention CNN and Remote Sensing Observations.” International Journal of Remote Sensing 42 (20): 7798–7820. doi:10.1080/01431161.2021.1966853.
  • Kingma, D. P., and B. Jimmy. (2014). “Adam: A Method for Stochastic Optimization .” ArXiv E-Prints, December, arXiv:1412.6980. doi:10.48550/arXiv.1412.6980.
  • Kriti, R., P. Bodani, and S. A. Sharma. 2022. “Automatic Building Footprint Extraction from Very High-Resolution Imagery Using Deep Learning Techniques.” Geocarto International 37 (5): 1501–1513. doi:10.1080/10106049.2020.1778100.
  • Ledig, C., L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, et al. 2017. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 105–114. 10.1109/CVPR.2017.19.
  • Liu, Y., D. Minh Nguyen, N. Deligiannis, W. Ding, and A. Munteanu. 2017. “Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery.” Remote Sensing 9 (6): 522. doi:10.3390/rs9060522.
  • Loveland, T. R., and A. S. Belward. 1997. “The International Geosphere Biosphere Programme Data and Information System Global Land Cover Data Set (DISCover).” Acta Astronautica 41 (4–10): 681–689. doi:10.1016/S0094-5765(98)00050-2.
  • Luo, X., X. Tong, Z. Qian, H. Pan, and S. Liu. 2019. “Detecting Urban Ecological Land-Cover Structure Using Remotely Sensed Imagery: A Multi-Area Study Focusing on Metropolitan Inner Cities.” International Journal of Applied Earth Observation and Geoinformation 75 (March): 106–117. doi:10.1016/j.jag.2018.10.014.
  • Maggiori, E., Y. Tarabalka, G. Charpiat, and P. Alliez. 2017. “Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (2): 645–657. doi:10.1109/TGRS.2016.2612821.
  • Marconcini, M., A. Metz-Marconcini, T. Esch, and N. Gorelick. 2021. “Understanding Current Trends in Global Urbanisation - the World Settlement Footprint Suite.” GI_Forum 1: 33–38. doi:10.1553/giscience2021_01_s33.
  • Marconcini, M., A. Metz-Marconcini, S. Üreyen, D. Palacios-Lopez, W. Hanke, F. Bachofer, J. Zeidler, et al. 2020. “Outlining Where Humans Live, the World Settlement Footprint 2015.” Scientific Data 7 (1): 242. doi:10.1038/s41597-020-00580-5.
  • Mnih, V. 2013. Machine Learning for Aerial Image Labeling. University of Toronto, Canada: University of Toronto (Canada). ISBN:978-0-494-96184-1.
  • Nah, S., T. Hyun Kim, and K. Mu Lee. 2017. “Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 257–265. Honolulu, HI, USA, doi:10.1109/CVPR.2017.35.
  • Okolie, C. J., and J. L. Smit. 2022. “A Systematic Review and Meta-Analysis of Digital Elevation Model (DEM) Fusion: Pre-Processing, Methods and Applications.” ISPRS Journal of Photogrammetry and Remote Sensing 188 (June): 1–29. doi:https://doi.org/10.1016/j.isprsjprs.2022.03.016.
  • Penglei, X., H. Tang, G. Jiayi, and L. Feng. 2021. “ESPC_NASUnet: An End-To-End Super-Resolution Semantic Segmentation Network for Mapping Buildings from Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 5421–5435. doi:10.1109/JSTARS.2021.3079459.
  • Pesaresi, M., A. Gerhardinger, and F. Kayitakire. 2009. “A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 (3): 180–192. doi:10.1109/JSTARS.2008.2002869.
  • Rimal, B., S. Sloan, H. Keshtkar, R. Sharma, S. Rijal, and U. Babu Shrestha. 2020. “Patterns of Historical and Future Urban Expansion in Nepal.” Remote Sensing 12 (4): 628. doi:10.3390/rs12040628.
  • Shi, W., J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang. 2016. “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1874–1883. IEEE. 10.1109/CVPR.2016.207.
  • Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” CoRr 1556: abs/1409.
  • Sirko, W., S. Kashubin, M. Ritter, A. Annkah, Y. Salah Eddine Bouchareb, Y. Dauphin, D. Keysers, M. Neumann, M. Cisse, and J. Quinn. 2021. “Continental-Scale Building Detection from High Resolution Satellite Imagery .” ArXiv E-Prints, July, arXiv:2107.12283. doi:10.48550/arXiv.2107.12283.
  • Sun, Ke, Bin Xiao, Dong Liu, and Jingdong Wang. 2019. “Deep High-Resolution Representation Learning for Human Pose Estimation.” ArXiv E-Prints, February, arXiv:1902.09212. doi:10.48550/arXiv.1902.09212.
  • Tateishi, R., B. Uriyangqai, H. Al-Bilbisi, M. Aboel Ghar, J. Tsend-Ayush, T. Kobayashi, A. Kasimu, et al. 2011. “Production of Global Land Cover Data – GLCNMO.” International Journal of Digital Earth 4 (1): 22–49. doi:10.1080/17538941003777521.
  • Xiao, F. 2018. “Multi-Sensor Data Fusion Based on a Generalised Belief Divergence Measure.” Information Fusion 46 (June): 23–32. doi:10.1016/j.inffus.2018.04.003.
  • Yang, S. 2018. How to Extract Building Footprints from Satellite Images Using Deep Learning. Microsoft Azure. https://azure.microsoft.com/es-es/blog/how-to-extract-building-footprints-from-satellite-images-using-deep-learning/.
  • Yang, L., S. Jin, P. Danielson, C. Homer, L. Gass, S. M. Bender, A. Case, et al. 2018. “A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies.” ISPRS Journal of Photogrammetry and Remote Sensing 146 (December): 108–123. doi:10.1016/j.isprsjprs.2018.09.006.
  • Yang, N., and H. Tang. 2020. “GeoBoost: An Incremental Deep Learning Approach Toward Global Mapping of Buildings from VHR Remote Sensing Images.” Remote Sensing 12 (11): 1794. doi:10.3390/rs12111794.
  • Yongyang, X., W. Liang, Z. Xie, and Z. Chen. 2018. “Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters.” Remote Sensing 10 (1): 144. doi:10.3390/rs10010144.
  • Yuan, J. 2018. “Learning Building Extraction in Aerial Scenes with Convolutional Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (11): 2793–2798. doi:10.1109/TPAMI.2017.2750680.
  • Zanaga, D., R. Van De Kerchove, W. De Keersmaecker, N. Souverijns, C. Brockmann, R. Quast, J. Wevers, C. B. Murray, S. Bals, and A. van Blaaderen. 2021. “Quantitative 3D Real-Space Analysis of Laves Phase Supraparticles.” Nature Communications 12(October). doi:10.5281/ZENODO.5571936.
  • Zhang, Y., L. Kunpeng, L. Kai, L. Wang, B. Zhong, and F. Yun 2018. “Image Super-Resolution Using Very Deep Residual Channel Attention Networks.” In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 286–301 doi:10.1007/978-3-030-01234-2_18.
  • Zhang, Z., Z. Qian, T. Zhong, M. Chen, K. Zhang, Y. Yang, R. Zhu, et al. 2022. “Vectorized Rooftop Area Data for 90 Cities in China.” Scientific Data 9 (1): 66. doi:10.1038/s41597-022-01168-x.
  • Zhang, T., H. Tang, Y. Ding, L. Penglong, J. Chao, and X. Penglei. 2021. “FSRSS-Net: High-Resolution Mapping of Buildings from Middle-Resolution Satellite Images Using a Super-Resolution Semantic Segmentation Network.” Remote Sensing 13 (12): 2290. doi:10.3390/rs13122290.
  • Zoph, B., V. Vasudevan, J. Shlens, and Q. V. Le. 2018. “Learning Transferable Architectures for Scalable Image Recognition.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 8697–8710 doi:10.1109/CVPR.2018.00907.