Publications

I started co-authoring scientific papers since 2018, during these years I collaborated with many researchers

Books

• Del Rosso, Maria Pia; Sebastianelli, Alessandro; Ullo, Silvia Liberata (ed.): ‘Artificial Intelligence Applied to Satellite-based Remote Sensing Data for Earth Observation’ (Telecommunications, 2021) DOI: IET Digital Library. [bibtex, book]

Papers

2024

• Russo, L., Mauro, F., Memar, B., Sebastianelli, A., Gamba, P., & Ullo, S. L. (2024). Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping. Accepted in IGARS2024. arXiv preprint arXiv:2402.00023. [bibtex, paper]

• Mauro, F., Sebastianelli, A., Saux, B. L., Gamba, P., & Ullo, S. L. (2024). A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction. Accepted in IGARS2024. arXiv preprint arXiv:2401.16049. [bibtex, paper]

• Mauro, F., Sebastianelli, A., Del Rosso, M. P., Gamba, P., & Ullo, S. L. (2024). QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering. Accepted in IGARS2024. arXiv preprint arXiv:2402.01235. [bibtex, paper]

• Sebastianelli, A., Spiller, D., Carmo, R., Wheeler, J., Nowakowski, A., Jacobson, L. V., … & Schneider, R. (2024). A reproducible ensemble machine learning approach to forecast dengue outbreaks. Scientific Reports, 14(1), 3807. [bibtex, paper]

• De Falco, F., Ceschini, A., Sebastianelli, A., Saux, B. L., & Panella, M. (2024). Towards Efficient Quantum Hybrid Diffusion Models. arXiv preprint arXiv:2402.16147. [bibtex, paper]

2023

• Jamila Mifdal, Marc Tomás-Cruz, Alessandro Sebastianelli, Bartomeu Coll, Joan Duran; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 2105-2114 . [bibtex, paper]

• Sebastianelli, A., Del Rosso, M. P., Ullo, S. L., & Gamba, P. (2023). On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data. [bibtex, paper]

• Mauro, F., Rich, B., Muriga, V. W., Sebastianelli, A., & Ullo, S. L. (2023). SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis. arXiv preprint arXiv:2301.07452. [bibtex, paper]

• Muriga, V. W., Rich, B., Mauro, F., Sebastianelli, A., & Ullo, S. L. (2023). A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset. arXiv preprint arXiv:2302.02440. [bibtex, paper]

• Mauro, F., Russo, L., Janku, F., Sebastianelli, A., & Ullo, S. L. (2023). Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting. arXiv preprint arXiv:2304.04069. [bibtex, paper]

• Spiller, D., Santin, G., Sebastianelli, A., Lucchini, L., Gallotti, R., Lake, B., … & Lepri, B. (2023). Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables. arXiv preprint arXiv:2302.14649. [bibtex, paper]

2022

• M. P. Del Rosso, A. Sebastianelli, D. Spiller and S. L. Ullo, “A demo setup testing onboard CNNs for Volcanic Eruption Detection”, in IEEE Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) [bibtex, paper]

• P. Di Stasio, A. Sebastianelli, G. Meoni and S. L. Ullo, “Early Detection of Volcanic Eruption through Artificial Intelligence on board”, in IEEE Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) [bibtex, paper]

• A. Sebastianelli, M. P. D. Rosso, S. L. Ullo and P. Gamba, “A Speckle Filter for Sentinel-1 SAR Ground Range Detected Data Based on Residual Convolutional Neural Networks,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5086-5101, 2022, doi: 10.1109/JSTARS.2022.3184355. [bibtex, paper]

• A. Sebastianelli et al., “PLFM: Pixel-Level Merging of Intermediate Feature Maps by disentangling and fusing spatial and temporal data for Cloud Removal,” in IEEE Transactions on Geoscience and Remote Sensing, 2022, doi: 10.1109/TGRS.2022.3208694. [bibtex, paper]

• Sebastianelli, A., Mauro, F., Di Cosmo, G., Passarini, F., Carminati, M., & Ullo, S. L. (2022, July). A Decision Support System Based on Machine Learning to Counteract Covid-Like Pandemic Events. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 4486-4489). IEEE. [bibtex, paper]

2021

• A. Sebastianelli, D. A. Zaidenberg, D. Spiller, B. L. Saux and S. L. Ullo, “On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 565-580, 2022, doi: 10.1109/JSTARS.2021.3134785. [bibtex, paper]

• D. A. Zaidenberg, A. Sebastianelli, D. Spiller, B. Le Saux and S. L. Ullo, “Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 5680-5683, doi: 10.1109/IGARSS47720.2021.9553133. [bibtex, paper]

• Del Rosso, Maria P., Alessandro Sebastianelli, Dario Spiller, Pierre P. Mathieu, and Silvia L. Ullo. 2021. “On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery” Remote Sensing 13, no. 17: 3479. [bibtex, paper]

• Sebastianelli, A., Del Rosso, M. P., & Ullo, S. L. (2021). Automatic dataset builder for Machine Learning applications to satellite imagery. SoftwareX, 15, 100739. [bibtex, paper]

• A. Sebastianelli, M. Tipaldi, S. L. Ullo and L. Glielmo, “A Deep Q-Learning based approach applied to the Snake game,” 2021 29th Mediterranean Conference on Control and Automation (MED), 2021, pp. 348-353, doi: 10.1109/MED51440.2021.9480232. [bibtex, paper]

• S. L. Ullo et al., “A New Mask R-CNN-Based Method for Improved Landslide Detection,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 3799-3810, 2021, doi: 10.1109/JSTARS.2021.3064981. [bibtex, paper]

• Sebastianelli A, Mauro F, Di Cosmo G, Passarini F, Carminati M, Ullo SL. AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing. ISPRS International Journal of Geo-Information. 2021; 10(1):34. [bibtex, paper]

• Schneider, R., Alessandro, S., Spiller, D., Wheeler, J., Carmo, R., Nowakowski, A., … & Lowe, R. (2021). Climate-based ensemble machine learning model to forecast Dengue epidemics (papers track). In Climate Change AI Workshop. [bibtex, paper]

2020

• T. De Corso et al., “Application of Dinsar Technique to High Coherence Satellite Images for Strategic Infrastructure Monitoring,” IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 4235-4238, doi: 10.1109/IGARSS39084.2020.9323810. [bibtex, paper]

2019

• S. L. Ullo et al., “Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data,” IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 9646-9649, doi: 10.1109/IGARSS.2019.8898632. [bibtex, paper]

2018

• S. L. Ullo et al., “SAR interferometry with open Sentinel-1 data for environmental measurements: The case of Ischia earthquake,” 2018 IEEE International Conference on Environmental Engineering (EE), 2018, pp. 1-8, doi: 10.1109/EE1.2018.8385270. [bibtex, paper]

• D. Di Martire et al., “X- and C-band SAR data to monitoring ground deformations and slow-moving landslides for the 2016 Manta and Portoviejo earthquake (Manabi, Ecuador),” 2018 IEEE International Conference on Environmental Engineering (EE), 2018, pp. 1-6, doi: 10.1109/EE1.2018.8385258. [bibtex, paper]