Dr. Hao Li

Technical University of Munich

hao_bgd.li@tum.de & hao.li@uni-heidelberg.de


Publications

Feature Papers

Li, H., Wang, J., Zollner, J. M., Mai, G., N., Lao. & Werner, M. (2023). Rethink Geographical Generalizability with Unsupervised Self-Attention Model Ensemble: A Case Study of OpenStreetMap Missing Building Detection in Africa. In Proceedings of the 31th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL ‘23), 2023

Yuan, Z., Kerckhoffs, J., Li, H., Khan, J., Hoek, G., & Vermeulen, R. (2024). Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring. Environmental Science & Technology, 2024.

Li, H., Wang, J., Teuscher, B., Luo, P., Hong, D., Mai, G., & Werner, M. (2024). GIMI: A Geographical Generalizable Image-to-Image Search Engine with Location-explicit Contrastive Embedding. ICLR 2024 Workshop on Machine Learning in Remote Sensing(ML4RS)

Hong, D., Zhang, B., Li, H., Y Li, J Yao, C Li, Werner, M., J Chanussote, J., Zipf, A., Zhu, XX. (2023). Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks. Remote Sensing of Environment, October 2023.

Dissertation

Li, H. (2022) Deep Learning from Heterogeneous Geospatial Data for Automated Mapping of Man-made Infrastructures with OpenStreetMap, Doctoral Thesis, Heidelberg University, 2022

Journal Papers (peer reviewed)

(J17) Yuan, Z., Kerckhoffs, J., Li, H., Khan, J., Hoek, G., & Vermeulen, R. (2024). Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring. Environmental Science & Technology, 2024.

(J16) Hong, D., Zhang, B., Li, X., Li, Y., Li, C., Yao, J., Yokoya, N., Li, H., Ghamisi, P., Jia, X., Plaza, A., Gamba, P., Benediktsson, J.A., & Chanussot, J. (2024). SpectralGPT: Spectral Foundation Model. IEEE Transactions on Pattern Analysis and Machine Intelligence (In press), 2024

(J15) Liu, Z., Fang, C., Li, H., Wu, J., Zhou, L., Werner, M. (2023). Efficiency and equality of the multimodal travel between public transit and bike-sharing accounting for multiscale. Sustainable Cities and Society, 101, 105096, 2023.

(J14) Hong, D., Zhang, B., Li, H., Y Li, J Yao, C Li, Werner, M., J Chanussote, J., Zipf, A., Zhu, XX. (2023). Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks. Remote Sensing of Environment, October 2023.

(J13) Hu, X., Zhou, Z., Li, H., Hu, Y., Gu, F., Kersten, J., Fan, H., & Klan, F. (2023). Location reference recognition from texts: A survey and comparison. ACM Computing Surveys.

(J12) Knoblauch, S., Li, H., Lautenbach, S., Elshiaty, Y., Rocha, A., Resch, B., Arifi, D., Jänish, T., Morales, I., Zipf, A. (2023) Semi-supervised water tank detection to support vector control of emerging infectious diseases transmitted by Aedes Aegypti, in International Journal of Applied Earth Observation and Geoinformation, April 2023.

(J11) Dax, G., Nagarajan, S., Li, H. and Werner, M., (2022) Compression Supports Spatial Deep Learning, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, December 2022.

(J10) Li, H., Yuan, Z., Novack, T., Huang, W., Zipf, A., (2022) Understanding spatiotemporal trip purposes of urban micro-mobility from the lens of dockless e-scooter sharing. Computers, Environment and Urban Systems, 96, 101848, June 2022

(J9) Li, H., Zech, J., Hong, D., Ghamisi, P., Schultz, M., Zipf, A.(2022) Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection. Volume 110, June 2022, 102804, International Journal of Applied Earth Observation and Geoinformation.

(J8) Li, H.; Herfort, B.; Lautenbach, S.; Chen, J.; Zipf, A.(2022) Improving OpenStreetMap missing building detection using few-shot transfer learning in sub-Saharan Africa. Transactions in GIS. 2022, 04 May. (Cover Image Article)

(J7) Li, H.; Zech, J.; Ludwig, C.; Fendrich, S.; Shapiro, A.; Schultz, M.; Zipf, A.(2021) Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning. International Journal of Applied Earth Observation and Geoinformation. 2021, Vol 104.

(J6) Hu, X.; Noskov, A.; Fan, H.; Novack, T.; Li, H.; Gu, F.; Shang, J.; Zipf, A.(2021) Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning. International Journal of Geographical Information Science, 2021, 04 Feb.

(J5) Li, H.; Ghamisi, P.; Rasti, B.; Wu, Z.; Shapiro, A.; Schultz, M.; Zipf, A.(2020) A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks. Remote Sensing. 2020, 12, 2067.

(J4) Li, H.; Herfort, B.; Wei, H.; Zia. M.; Zipf, A. (2020) Exploration of OpenStreetMap Missing Built-up Areas using Twitter Hierarchical Clustering and Deep Learning in Mozambique. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 166, August 2020, Pages 41-51. (Featured paper in August)

(J3) Zhu X., Hu J., Qiu C., Shi Y., Kang J., Mou L., Bagheri H., Härberle M., Hua Y., Huang R., Hughes L., Li H., Sun Y., Zhang G., Han S., Schmitt M., Wang Y., (2020) So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification, IEEE Geoscience and Remote Sensing Magazine. Volume 8, Issue: 3, Sept. 2020.

(J2) Herfort, B.; Li, H..; Fendrich, S.; Lautenbach, S.; Zipf, A. (2019) Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning. Remote Sensing. 2019, 11, 1799.

(J1) Li, H.., Ghamisi, P., Soergel, U. and Zhu, X.X. (2018): Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks. Remote Sensing, 2018, 10, 1649.

Editorials (peer reviewed)

(E4) Grinberger, A.Y., Li, H., Liu, P., Yeboah, G., Juhász, L., Coetzee, S., Mooney, P., Sarretta, A., Anderson, J., & Minghini, M. (2023). OpenStreetMap as an Emerging Scientific Discipline: Reflections from the OSM Science 2023 In: Minghini, M., Li, H., Grinberger, A.Y., Liu, P., Yeboah, G., Juhász, L., Coetzee, S., Mooney, P., Sarretta, A., & Anderson, J., & Minghini, M.(Eds.). Proceedings of the OSM Science at State of the Map Europe 2023, Antwerp, Belgium, 10-12 November 2023. Available at https://zenodo.org/communities/osmscience-2023

(E3) Huang, W., Chen, B. Y., Biljecki, F., Yan, Y., Grinberger, Y., & Li, H. (2023). Preface: Workshop “GeoHB 2023: Geo-Spatial Computing for Understanding Human Behaviours”. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 1985-1986.

(E2) Li, H., Cavallaro, G., Heras, D. B., Lunga, D., Werner, M., & Züfle, A. (2023) Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data (GeoSearch ‘23)

(E1) Grinberger, A.Y., Liu, P., Li, H., Juhász, L., & Minghini, M. (2022). OpenStreetMap, beyond just Data: The Academic Track at State of the Map 2022. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22, DOI: 10.5281/zenodo.7004424

Conference Proceedings (peer reviewed)

(C13) Li, H., Wang, J., Teuscher, B., Luo, P., Hong, D., Mai, G., & Werner, M. (2024). GIMI: A Geographical Generalizable Image-to-Image Search Engine with Location-explicit Contrastive Embedding. ICLR 2024 Workshop on Machine Learning in Remote Sensing(ML4RS)

(C13) Li, H., & Sun, Y. (2023). Beyond Two Dimensions: Large-Scale Building Height Mapping in OpenStreetMap via Synthetic Aperture Radar and Street-View Imagery. Proceedings of the OSM Science at State of the Map Europe 2023, Antwerp, Belgium, 10-12 November 2023. Available at https://zenodo.org/communities/osmscience-2023

(C12) Li, H., Wang, J., Zollner, J. M., Mai, G., N., Lao. & Werner, M. (2023). Rethink Geographical Generalizability with Unsupervised Self-Attention Model Ensemble: A Case Study of OpenStreetMap Missing Building Detection in Africa. In Proceedings of the 31th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL ‘23), 2023

(C11) Li, H., Yuan, Z., Dax, G., Fan, H., Zipf, A. & Werner, M. (2023). Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. The 12 International Conference on Geographic Information Science. 12 - 15th September, 2023. Leeds, UK (GISicence 2023 Full Paper)

(C10) Luo, X., Walther, P., Mansour, W., Teuscher, B., Zollner, J. M., Li, H., & Werner, M (2023). Exploring GeoAI Methods for Supraglacial Lake Mapping on Greenland Ice Sheet. In Proceedings of the 31th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL ‘23), 2023

(C9) Werner, M., Li, H., Zollner, J. M., Teuscher, B., & Deuser., F. (2023). Bavaria Buildings - A Novel Dataset for Building Footprint Extraction, Instance Segmentation, and Data Quality Estimation (Data and Resources Paper). Proceedings of the 31st International Conference on Advances in Geographic Information Systems (ACM SIGSPATAIL GIS’23).

(C8) Teuscher, B., Geißendörfer, O., Luo, X., Li, H., Anders, K., Holst, C., & Werner, M. (2023). Efficient In-Memory Point Cloud Query Processing. 18th International 3DGeoInfo Conference 2023.

(C7) Liu, Z., Wu, J., Li, H., & Werner, M. (2023). Spatio-temporal Analysis of Urban Economic Resilience during Covid-19 with Multilayer Complex Networks. The ISPRS Geospatial Week 2023, the GeoHB 2023 Workshop.

(C6) Werner, M. and Li, H. 2022. AtlasHDF: an efficient big data framework for GeoAI. In Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial ‘22). Association for Computing Machinery, New York, NY, USA, 1–7.

(C5) Li, H.; Zipf, A. (2022) A conceptual model for converting OpenStreetMap contribution to geospatial machine learning training data, ISPRS XXIV Congress 2022 At: Nice, France. (the ISPRS Best Poster Award)

(C4) Pisl, J., Li, H., Lautenbach, S., Herfort, B., and Zipf, A. (2021): Detecting OpenStreetMap missing buildings by transferring pre-trained deep neural networks, AGILE GIScience Ser., 2, 39, 2021.

(C3) Ghamisi, P., Li, H., Jackisch, R., Rasti, B., Gloaguen, R. (2019): Remote Sensing and Deep Learning for Sustainable Mining, In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 3739-3742

(C2) Wu, Z., Li, H.*, & Zipf, A. (2020): From Historical OpenStreetMap data to customized training samples for geospatial machine learning, In: Proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. (*: Corresponding Author)

(C1) Li, H., Herfort, B., Zipf, A. (2019): Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks, In: Proceedings of the 22st AGILE Conference on Geographic Information Science, Limassol, Cyprus.

Others

Li, H., Herfort, B., Fendrich, S., Lautenbach, S., Zipf, A. (2019): Künstliche Intelligenz und Crowdsourcing Geoinformation zur Unterstützung Humanitärer Hilfe, gis.Business, Volume 2019, Issue 2, 2019, Pages 51-52

Li, H., Herfort, B., Zia, M., Zipf, A. (2020): Geo-Social-Media und Künstliche Intelligenz zur Unterstützung von humanitärer Hilfe in Afrika, gis.Business, Volume 2020, Issue 4, 2020, Pages 33-34