Doctor of Engineering in Geoscience, Technical University of Munich, 04/2019-11/2022

Master of Science in Earth Oriented Space Science and Technology, Technical University of Munich, 10/2016 – 11/2018

Master of Engineering in Photogrammetry and Remote Sensing, Wuhan University, 09/2015 – 06/2019

Bachelor of Engineering in Remote Sensing Science and Technology, Wuhan University, 09/2011 – 06/2015

Qingyu Li received the bachelor’s degree in Remote Sensing Science and Technology from Wuhan University, China, in 2015, the master’s degree in Earth Oriented Space Science and Technology (ESPACE) from Technical University of Munich (TUM), Germany, in 2018, the master’s degree in Photogrammetry and Remote Sensing from Wuhan University in 2019, and the Doctor of Engineering (Dr.-Ing.) degree in Geoscience from TUM in 2022. From 2019 to 2022, she was a Research Associate with TUM and the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Germany. From 2022 to 2024, she was a Post-Doctoral Researcher with TUM. Her research interests include artificial intelligence, remote sensing, computer vision, and geospatial applications. She has published over 30 papers, including 10+ first-authored SCI papers in journals such as IEEE Transactions on Geoscience and Remote Sensing, International Journal of Applied Earth Observation and Geoinformation, Applied Energy, Sustainable Cities and Society, etc. In 2020, she won the Geodesy Award from the German Association for Geodesy, Geoinformation and Land Management. She has served as a guest editor of the SCI journal Remote Sensing and the session chair of the International Geoscience and Remote Sensing Symposium (IGARSS).

Book chapters

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.

Journal papers

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.

Conference papers

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.