1. [1] Lin, J., Jing, W., Song, H., Chen, G. "ESFNet: Efficient Network for Building Extraction From High-Resolution Aerial Images," in IEEE Access, vol. 7, pp. 54285-54294, 2019, doi: 10.1109/ACCESS.2019.2912822.

2. [2] Musse, M.A., Barona, D.A., Rodriguez, L.M.S. "Urban environmental quality assessment using remote sensing and census data," International Journal of Applied Earth Observation and Geoinformation, vol. 71, PP. 95-108, 2018, [DOI:10.1016/j.jag.2018.05.010]

3. [3] Agarwal, L., Rajan, K. S. 2015. Fast ICA based algorithm for building detection from VHR imagery. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 10.1109/IGARSS.2015.7326162.

4. [4] Feng, W., Sui, H., Hua, L.,Xu, C., Ma, G., Huang, W. 2020. Building extraction from VHR remote sensing imagery by combining an improved deep convolutional encoder-decoder architecture and historical land use vector map. International Journal of Remote Sensing, 41(17), 6595-6617. [DOI:10.1080/01431161.2020.1742944]

5. [5] Huang, J., Xia, G.S., Hu, F., Zhang, L. 2018. Accurate building detection in VHR remote sensing images using geometric saliency. IGRASS'18 conference paper. [DOI:10.1109/IGARSS.2018.8517331]

6. [6] Wang, X., Li, P. Extraction of urban building damage using spectral, height and corner information from VHR satellite images and airborne LiDAR data, ISPRS Journal of Photogrammetry and Remote Sensing, vol.159, 2020, pp. 322-336, [DOI:10.1016/j.isprsjprs.2019.11.028]

7. [7] You, Y., Wang, S., Ma, Y., Chen, G., Wang, B., Shen, M., Liu, W. 2018. Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index. Journal of remote sensing, 10, 1287, https://doi:10.3390/rs10081287.

8. [8] Li, J., Huang, X., Tu, L., Zhang, T. and Wang, L. 2022. A review of building detection from very high resolution optical remote sensing images. Journal of GISCIENCE & REMOTE SENSING, 59(1), 1199-1225. [DOI:10.1080/15481603.2022.2101727]

9. [9] Wang, S., Hou, X., Zhao, X. "Automatic Building Extraction From High-Resolution Aerial Imagery via Fully Convolutional Encoder-Decoder Network With Non-Local Block," in IEEE Access, vol. 8, pp. 7313-7322, 2020, doi: 10.1109/ACCESS.2020.2964043.

10. [10] Liu, Y., Zhou, J., QI, W., Li, X. et al. "ARC-Net: An Efficient Network for Building Extraction From High-Resolution Aerial Images," in IEEE Access, vol. 8, pp. 154997-155010, 2020, doi: 10.1109/ACCESS.2020.3015701.

11. [11] Bittner, K., Adam, F., Cui, S., Korner, M., Reinartz, P. 2018. Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 11(8), 2615-2629, [DOI:10.1109/JSTARS.2018.2849363]

12. [12] Zeng, Y., Guo, Y., Li, J. 2022. Recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning. Journal of Neural Computing and Applications, 34, 2691-2706. [DOI:10.1007/s00521-021-06027-1]

13. [13] Zhu, Q., Liao, C., Hu, H., Mei, X., Li, H. "MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 7, pp. 6169-6181, July 2021, doi: 10.1109/TGRS.2020.3026051.

14. [14] Liao, C.m Hu, H., Li, H., Ge, X., Chen, M., Li, C., Zhu, Q. "Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction," Remote Sens. 2021, 13, 1049. [DOI:10.3390/rs13061049]

15. [15] Lecun, Y., Bottou, L., Bengio, Y., Haffner, P. "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998, doi: 10.1109/5.726791.

16. [16] Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A. " Deep learning in remote sensing applications: A meta-analysis and review," ISPRS Journal of Photogrammetry and Remote Sensing 152, pp. 166-177, 2019. DOI:10.1016/j.isprsjprs.2019.04.015.

17. [17] Yoo, H.J. "Deep Convolution Neural Networks in Computer Vision: a Review," IEIE Transactions on Smart Processing and Computing, vol. 4, no. 1, 2015, pp. 35-43. http://dx.doi.org/10.5573/IEIESPC.2015.4.1.035.

18. [18] Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J. "A Review on Deep Learning Techniques Applied to Semantic Segmentation ," Computer visión and Pattern recognition, 2017. [DOI:10.1016/j.asoc.2018.05.018]

19. [19] Ronneberger, O., Fischer, P., Brox, T. " U-Net: Convolutional Networks for Biomedical Image Segmentation," Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part III, LNCS 9351, pp. 234-241, 2015. DOI: 10.1007/978-3-319-24574-4_2.

20. [20] Hamaguchi, R., Fujita, A., Nemoto, K., Imaizumi, T., Hikosaka, S. " Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery," Computer visión and Pattern recognition, 2017. HTTPS://DOI.ORG/10.48550/ARXIV.1709.00179.

21. [21] Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L. "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs," IEEE Transactions on Pattern Analysis and Machine Intelligence PP(99), 2016. DOI:10.1109/TPAMI.2017.2699184.

22. [22] Shao, Z., Tang, P., Wang, Z., Saleem, N., Yam, S., Sommai, C. "BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images," Remote Sensing 12(6):1050, 2020. DOI:10.3390/rs12061050.

23. [23] Shelhamer, E., Long, J., Darrell, T. "Fully Convolutional Networks for Semantic Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,

24. [24] Liu, Y., Minh Nguyen, D., Deligiannis, N., Ding, W., Munteanu, A. "Hourglass-shape network based semantic segmentation for high resolution aerial imagery," Remote Sens. 2017, 9, 522.

25. [25] Lin, G., Milan, A., Shen, C., Reid, I. "RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation," 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5168-5177. DOI 10.1109/CVPR.2017.549.

26. [26] Pan, X., Yang, F., Gao, L., Chen, Z., Zhang, B., Fan, H., Ren, J. " Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms," Remote Sens. 2019, 11, 917; doi:10.3390/rs11080917.

27. [27] Xu, Y., Wu, L., Xie, Z., Chen, Z. "Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters," Remote Sens. 2018, 10, 144; doi:10.3390/rs10010144.

28. [28] Wang, S., Hou, X., Zhao, X. "Automatic Building Extraction From High-Resolution Aerial Imagery via Fully Convolutional Encoder-Decoder Network With Non-Local Block," in IEEE Access, vol. 8, pp. 7313-7322, 2020, doi: 10.1109/ACCESS.2020.2964043.

29. [29] Wei, S., Ji, S., Lu, M. "Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 3, pp. 2178-2189, March 2020, doi: 10.1109/TGRS.2019.2954461.

30. [30] Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., and Zhao, Z. " Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network," Remote Sens. 2019, 11, 1774; doi:10.3390/rs11151774.

31. [31] Yang, H. L., Yuan, J., Lunga, D., Laverdiere, M., Rose, A., Bhaduri, B. "Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 8, pp. 2600-2614, Aug. 2018, doi: 10.1109/JSTARS.2018.2835377.

32. [32] GUO, Z., SHI, X., ZHANG, H., HUANG, D., SONG, X., YAN, J., SHIBASAKI, R. "Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets," Computer Vision and Pattern Recognition, 2023. HTTPS://DOI.ORG/10.48550/ARXIV.2307.04101

33. [33] Chen, L.C., Papandreou, G., Schroff, F., Adam, H. "Rethinking Atrous Convolution for Semantic Image Segmentation," Computer Vision and Pattern Recognition, 2017.

34. [34] Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J. "Pyramid Scene Parsing Network," Computer Vision and Pattern Recognition, 2016. [DOI:10.1109/CVPR.2017.660]

35. [35] Ji,Sh., Wei, Sh., Lu, M. "A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery," International Journal of Remote Sensing, 2018, DOI: 10.1080/01431161.2018.1528024.

36. [36] Zeyu, X., Zhanfeng, Sh., Yang, L., Lifang, Z., Yingming, K., Lingling, L., Qi, W. "Classification of high-resolution remote sensing images based on Enhanced DeepLab algorithm and adaptive loss function," Journal of Remote Sensing, 2021, DOI: 10.11834/jrs.20209200.

37. [37] Wang, X., Hu, Z., Shi, S. et al. "A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet," Sci Rep 13, 7600 (2023). [DOI:10.1038/s41598-023-34379-2]

38. [38] He, K., Zhang, X. Ren, S., Sun, J. "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.

39. [39] Chen, LC., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation," In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision - ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11211. Springer, Cham. [DOI:10.1007/978-3-030-01234-2_49]

40. [40] Ji, S., Wei, S., Lu, M. "Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 1, pp. 574-586, Jan. 2019, doi: 10.1109/TGRS.2018.2858817.

41. [41] Maggiori, E., Tarabalka,Y.,Charpiat, G., Alliez, P. "Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark," IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2017, Fort Worth, United States. ffhal-01468452.

42. [42] Kingma, D., Ba, J. "Adam: A Method for Stochastic Optimization," International Conference on Learning Representations. 2014.