prof.dr.ir. C. Persello (Claudio)

Earth & Environmental Sciences

# Detection
# Imagery
# Informal Settlement
# Learning
# Remote Sensing

Engineering & Materials Science

# Deep Learning
# Remote Sensing
# Unmanned Aerial Vehicles (Uav)

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Earth Observation Science (EOS)

Zhou, W. , Persello, C., Li, M. , & Stein, A. (2023). Building use and mixed-use classification with a transformer-based network fusing satellite images and geospatial textual information. Remote sensing of environment, 297, [113767]. https://doi.org/10.1016/j.rse.2023.113767

Kumar, V., Venkatachalaperumal, S. R. , & Persello, C. (2023). Synergistic fusion of spaceborne polarimetric SAR and hyperspectral data for land cover classification. In S. Kumar, P. Siqueira, H. Govil, & S. Agrawal (Eds.), Spaceborne Synthetic Aperture Radar Remote Sensing: Techniques and Applications (pp. 169-210). CRC Press/Balkema. https://doi.org/10.1201/9781003204466-8

Persello, C., Hansch, R., Vivone, G., Chen, K., Yan, Z., Tang, D., Huang, H., Schmitt, M., & Sun, X. (2023). 2023 IEEE GRSS data fusion contest: Large-scale fine-grained building classification for semantic urban reconstruction [technical committees]. IEEE geoscience and remote sensing magazine, 11(1), 94-97. https://doi.org/10.1109/MGRS.2023.3240233

Lv, X. , Persello, C. , Zhao, W., Huang, X., Hu, Z., Ming, D. , & Stein, A. (2023). Pruning for image segmentation: Improving computational efficiency for large-scale remote sensing applications. ISPRS journal of photogrammetry and remote sensing, 202, 13-29. https://doi.org/10.1016/j.isprsjprs.2023.05.024

Zhou, W. , Persello, C. , & Stein, A. (2023). Building usage classification using a transformer-based multimodal deep learning method. In 2023 Joint Urban Remote Sensing Event, JURSE 2023 (2023 Joint Urban Remote Sensing Event, JURSE 2023). IEEE. https://doi.org/10.1109/JURSE57346.2023.10144168

Zhao, W. , Persello, C., Ding, H. , & Stein, A. (2023). Learning general representations for semantic segmentation and height estimation from remote sensing images. In 2023 Joint Urban Remote Sensing Event (2023 Joint Urban Remote Sensing Event, JURSE 2023). IEEE. https://doi.org/10.1109/JURSE57346.2023.10144138

Grift, J. , Persello, C. , & Koeva, M. N. (2023). Cadastral boundary delineation using deep learning and remote sensing imagery: state of the art and future developments. In FIG Working Week 2023: Protecting Our World, Conquering New Frontiers International Federation of Surveyors (FIG). https://fig.net/fig2023/technical_program.htm

Zhao, W. , Persello, C. , & Stein, A. (2023). Semantic-aware unsupervised domain adaptation for height estimation from single-view aerial images. ISPRS journal of photogrammetry and remote sensing, 196, 372-385. https://doi.org/10.1016/j.isprsjprs.2023.01.003

Hansch, R. , Persello, C., Vivone, G., Castillo Navarro, J., Boulch, A., Lefevre, S., & Le Saux, B. (2022). Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised learning. IEEE geoscience and remote sensing magazine, 2-5. https://doi.org/10.1109/MGRS.2022.3219935

Zhao, W. (2022). Extracting geometric features of buildings from remote sensing images. [PhD Thesis - Research UT, graduation UT, Faculty of Geo-Information Science and Earth Observation, University of Twente]. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036554978

Koeva, M. N., Bennett, R. M. , & Persello, C. (2022). Remote sensing for land administration 2.0. Remote sensing, 14(17), [4359]. https://doi.org/10.3390/rs14174359

Farsad Layegh, N. , Darvishzadeh, R. , Skidmore, A. K. , Persello, C., & Kruger, N. (2022). Integrating semi-supervised learning with an expert system for vegetation cover classification using Sentinel-2 and RapidEye data. Remote sensing, 14(15), 1-18. [3605]. https://doi.org/10.3390/rs14153605

Zhao, W. , Persello, C. , & Stein, A. (2022). Extracting planar roof structures from very high resolution images using graph neural networks. ISPRS journal of photogrammetry and remote sensing, 187, 34-45. https://doi.org/10.1016/j.isprsjprs.2022.02.022

Hänsch, R. , Persello, C., Vivone, G., Castillo Navarro, J., Boulch, A., Lefevre, S., & Le Saux, B. (2022). The 2022 IEEE GRSS data fusion contest: Semisupervised learning [technical committees]. IEEE geoscience and remote sensing magazine, 10(1), 334-337. https://doi.org/10.1109/MGRS.2022.3144291

Persello, C., Wegner, J. D., Hansch, R., Tuia, D., Ghamisi, P. , Koeva, M., & Camps-Valls, G. (2022). Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities. IEEE geoscience and remote sensing magazine, 10(2), 172-200. https://doi.org/10.1109/MGRS.2021.3136100

Nex, F., Armenakis, C., Cramer, M., Cucci, D. A., Gerke, M., Honkavaara, E., Kukko, A. , Persello, C., & Skaloud, J. (2022). UAV in the advent of the twenties: Where we stand and what is next. ISPRS journal of photogrammetry and remote sensing, 184, 215-242. https://doi.org/10.1016/j.isprsjprs.2021.12.006

Kuffer, M., Grippa, T. , Persello, C., Taubenböck, H. , Pfeffer, K. , & Sliuzas, R. (2022). Mapping the morphology of urban deprivation. In X. Yang (Ed.), Urban Remote Sensing: Monitoring, Synthesis, and Modeling in the Urban Environment (pp. 305-323). Wiley. https://doi.org/10.1002/9781119625865.ch14

Mullissa, A. G. , Persello, C., & Reiche, J. (2022). Despeckling polarimetric SAR data using a multistream complex-valued fully convolutional network. IEEE geoscience and remote sensing letters, 19, 1-5. https://doi.org/10.1109/LGRS.2021.3066311

Zhao, W. , Persello, C. , & Stein, A. (2021). End-to-end roofline extraction from very-high-resolution remote sensing images. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 2783-2786). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9554162

Sun, X. , Zhao, W. , V. Maretto, R. , & Persello, C. (2021). Building polygon extraction from aerial images and digital surface models with a frame field learning framework. Remote sensing, 13(22), 1-21. [4700]. https://doi.org/10.3390/rs13224700

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