李清玉

助理教授

教育背景

博士（慕尼黑工业大学，地球科学，04/2019 – 11/2022）

硕士（慕尼黑工业大学，Earth Oriented Space Science and Technology，10/2016 – 11/2018）

硕士（武汉大学，摄影测量与遥感，09/2015 – 06/2019）

本科（武汉大学，遥感科学与技术，09/2011 – 06/2015）

研究领域
人工智能；遥感；计算机视觉；地理空间应用
学术领域
计算机工程，人工智能与机器人
个人网站
https://lqycrystal.github.io/qingyuli.github.io/
电子邮件
liqingyu@cuhk.edu.cn
个人简介

李清玉于2015年获得武汉大学遥感科学与技术学士学位，2018年获得德国慕尼黑工业大学（TUM）Earth Oriented Space Science and Technology (ESPACE)硕士学位，2019年获得武汉大学摄影测量与遥感硕士学位，2022年获得慕尼黑工业大学地球科学工程博士（Dr.-Ing.）学位。2019年至2022年，她曾在慕尼黑工业大学和德国航空航天中心（DLR）遥感技术研究所（IMF）开展研究。2022年至2024年，她是慕尼黑工业大学的博士后研究员。她的研究兴趣包括人工智能、遥感、计算机视觉和地理空间应用。她已发表论文30余篇，其中以第一作者在IEEE Transactions on Geoscience and Remote Sensing、International Journal of Applied Earth Observation and Geoinformation、Applied Energy、Sustainable Cities and Society等高水平SCI期刊发表10余篇论文。2020年获德国测绘、地理信息与土地管理协会的测绘奖。担任SCI期刊Remote Sensing的客座编委，国际遥感信息处理研讨会IGARSS的专题主席。

学术著作

书籍：

  1. Roschlaub, R., Glock, C., Möst, K., Li, Q., Auer, S., & Zhu, X. X.  (2022). “Mit Deep Learning und amtlichen Daten zur landesweiten Detektion von Gebäuden und Gebäudeveränderungen.” in Künstliche Intelligenz in Geodäsie und Geoinformatik - Potenziale und Best-Practice-Beispiele, edited by Grunau, Wilfried. Germany: Wichmann Verlag.

期刊论文：

  1. Li, Q., Krapf, S., Mou, L., Shi, Y., & Zhu, X. X. (2024). Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures. Applied Energy, 374, 123839.
  2. Li, Q., Xu, G., & Gu, Z. (2024). A novel framework for multi-city building energy simulation: Coupling urban microclimate and energy dynamics at high spatiotemporal resolutions. Sustainable Cities and Society, 113, 105718.
  3. Li, Q., Mou, L., Sun, Y., Hua, Y., Shi, Y., & Zhu, X. X. (2024). A Review of Building Extraction from Remote Sensing Imagery: Geometrical Structures and Semantic Attributes. IEEE Transactions on Geoscience and Remote Sensing, 62,1-15.
  4. Liu, C., Albrecht, C. M., Wang, Y., Li, Q., & Zhu, X. X. (2024). AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing.
  5. Li, Q., Mou, L., Hua, Y., Shi, Y., Chen, S., Sun, Y., & Zhu, X. X. (2023). 3DCentripetalNet: Building height retrieval from monocular remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 120, 103311.
  6. Li, Q., Krapf, S., Shi, Y., & Zhu, X. X. (2023). SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery. International Journal of Applied Earth Observation and Geoinformation, 116, 103098.
  7. Li, Q., Taubenböck, H., Shi, Y., Auer, S., Roschlaub, R., Glock, C., Kruspe, A., & Zhu, X. X. (2022). Identification of undocumented buildings in cadastral data using remote sensing: Construction period, morphology, and landscape. International Journal of Applied Earth Observation and Geoinformation, 112, 102909.
  8. Roschlaub, R., Glock, C., Möst, K., Hümmer, F., Li, Q., Auer, S., Kruspe, A., & Zhu, X. X. (2022). Implementierung einer KI-Infrastruktur zur automatisierten Erkennung von landesweiten Gebäudeveränderungen aus Luftbildern. ZfV-Zeitschrift für Geodäsie, Geoinformation und Landmanagement, (zfv 3/2022).
  9. Li, Q., Shi, Y., & Zhu, X. X. (2022). Semi-supervised building footprint generation with feature and output consistency training. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17.
  10. Li, Q., Zorzi, S., Shi, Y., Fraundorfer, F., & Zhu, X. X. (2022). RegGAN: An end-to-end network for building footprint generation with boundary regularization. Remote Sensing, 14(8), 1835.
  11. Li, Q., Mou, L., Hua, Y., Shi, Y., & Zhu, X. X. (2022). CrossGeoNet: A framework for building footprint generation of label-scarce geographical regions. International Journal of Applied Earth Observation and Geoinformation, 111, 102824.
  12. Li, Q., Mou, L., Hua, Y., Shi, Y., & Zhu, X. X. (2021). Building footprint generation through convolutional neural networks with attraction field representation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17.
  13. Li, Q., Shi, Y., Auer, S., Roschlaub, R., Möst, K., Schmitt, M., Glock, C., & Zhu, X. X. (2020). Detection of undocumented building constructions from official geodata using a convolutional neural network. Remote Sensing, 12(21), 3537.
  14. Li, Q., Shi, Y., Huang, X., & Zhu, X. X. (2020). Building footprint generation by integrating convolution neural network with feature pairwise conditional random field (FPCRF). IEEE Transactions on Geoscience and Remote Sensing, 58(11), 7502-7519.
  15. Li, Q., Qiu, C., Ma, L., Schmitt, M., & Zhu, X. X. (2020). Mapping the land cover of Africa at 10 m resolution from multi-source remote sensing data with Google Earth Engine. Remote Sensing, 12(4), 602.
  16. Roschlaub, R., Li, Q., Auer, S., Möst, K., Glock, C., Schmitt, M., Shi, Y & Zhu, X. X. (2020). KI-basierte Detektion von Gebäuden mittels Deep Learning und amtlichen Geodaten zur Baufallerkundung. ZFV-Zeitschrift für Geodasie, Geoinformation und Landmanagement, (3), 180-189.
  17. Shi, Y., Li, Q., & Zhu, X. X. (2020). Building segmentation through a gated graph convolutional neural network with deep structured feature embedding. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 184-197.
  18. Shi, Y., Li, Q., & Zhu, X. X. (2018). Building footprint generation using improved generative adversarial networks. IEEE Geoscience and Remote Sensing Letters, 16(4), 603-607.
  19. Li, Q., Huang, X., Wen, D., & Liu, H. (2017). Integrating multiple textural features for remote sensing image change detection. Photogrammetric Engineering & Remote Sensing, 83(2), 109-121.
  20. Huang, X., Li, Q., Liu, H., & Li, J. (2016). Assessing and improving the accuracy of GlobeLand30 data for urban area delineation by combining multisource remote sensing data. IEEE Geoscience and Remote Sensing Letters, 13(12), 1860-1864.

会议论文：

  1. Lin, W., Zhu, J., Hua, Y., Li, Q., Mou, L., & Zhu, X. X. (2024). Towards Sustainable Urban Energy: A Robust Deep Learning Framework for Solar Potential Estimation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 371-378.
  2. Li, Q., Krapf, S., Mou, L., Shi, Y., & Zhu, X. X. (2023). Roof superstructure detection from aerial imagery. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  3. Li, Q., Sun, Y., Mou, L., Shi, Y., & Zhu, X. X. (2023). Semi-supervised segmentation of individual buildings from SAR imagery. In 2023 Joint Urban Remote Sensing Event (JURSE). IEEE.
  4. Li, Q., Shi, Y., & Zhu, X. X. (2022). Feature and output consistency training for semi-supervised building footprint generation. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  5. Li, Q., Zorzi, S., Shi, Y., Fraundorfer, F., & Zhu, X. X. (2021). End-to-end semantic segmentation and boundary regularization of buildings from satellite imagery. In IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  6. Chen, S., Mou, L., Li, Q., Sun, Y., & Zhu, X. X. (2021). Mask-height R-CNN: An end-to-end network for 3D building reconstruction from monocular remote sensing imagery. In IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  7. Li, Q., Mou, L., Hua, Y., Sun, Y., Jin, P., Shi, Y., & Zhu, X. X. (2020). Instance segmentation of buildings using keypoints. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  8. Li, Q., Shi, Y., Auer, S., Roschlaub, R., Möst, K., Schmitt, M., & Zhu, X. X. (2020). Detection of Undocumented Buildings Using Convolutional Neural Network and Official Geodata. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 517-524.
  9. Shi, Y., Li, Q., & Zhu, X. X. (2020). Building extraction by gated graph convolutional neural network with deep structured feature embedding. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  10. Shi, Y., Li, Q., & Zhu, X. X. (2019). Building footprint extraction with graph convolutional network. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  11. Shi, Y., Li, Q., & Zhu, X. X. (2019). BFGAN–building footprint extraction from satellite images. In 2019 Joint Urban Remote Sensing Event (JURSE). IEEE.