Dahal, A., Tanyaș, H., & Lombardo, L. (2024). Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction. Communications Earth and Environment5(75), Article 75. Advance online publication.


Cisneros, D., Richards, J., Dahal, A., Lombardo, L., & Huser, R. (2023). Deep graphical regression for jointly moderate and extreme Australian wildfires.

Dahal, A., & Lombardo, L. (2023). Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modelingComputers & geosciences176, Article 105364.

Maslov, K. A., Persello, C., Schellenberger, T., & Stein, A. (2023). GLAVITU: A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 1233-1236). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). IEEE.

Persello, C., Grift, J., Fan, X., Paris, C., Hänsch, R., Koeva, M., & Nelson, A. (2023). AI4SmallFarms: A data set for crop field delineation in Southeast Asian smallholder farms. IEEE geoscience and remote sensing letters20, 1-5. Article 2505705.

Sedona, R., Paris, C., Ebert, J., Riedel, M., & Cavallaro, G. (2023). Toward the production of spatiotemporally consistent annual land cover maps using Sentinel-2 time seriesIEEE geoscience and remote sensing letters20, 1-5. Article 2505805. Advance online publication.

Weikmann, G., Marinelli, D., Paris, C., Migdall, S., Gleisberg, E., Appel, F., Bach, H., Dowling, J., & Bruzzone, L. (2023). Multi-year mapping of water demand at crop level: An end-to-end workflow based on high-resolution crop type maps and meteorological dataIEEE Journal of selected topics in applied earth observations and remote sensing16, 6758-6775.


Paris, C., Gasparella, L., & Bruzzone, L. (2022). A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian country case study. IEEE Journal of selected topics in applied earth observations and remote sensing15, 9146-9159.

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 magazine10(2), 172-200.


Zhao, W., Persello, C., & Stein, A. (2021). Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework. ISPRS journal of photogrammetry and remote sensing175, 119-131.


Persello, C., Tolpekin, V. A., Bergado, J. R., & de By, R. A. (2019). Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote sensing of environment231, 1-18. Article 111253.


Persello, C., & Stein, A. (2017). Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR ImagesIEEE geoscience and remote sensing letters14(12), 2325-2329.