Project Summary

Quantum Machine Learning for EO

This summary presents an overview of my research within the QC4EO framework, focusing on Quantum Machine Learning for Earth Observation (QML4EO). The work is structured around two main pillars: Applications and Foundations. On the Applications side, QML methods have been developed and tested for a range of EO tasks, including noise filtering in satellite imagery, hybrid quantum-classical models for land classification, object detection, image synthesis with quantum generative models, spatiotemporal forecasting using quantum recurrent networks, and pixel-wise segmentation of geospatial data. These projects demonstrate the potential of quantum-enhanced algorithms to tackle high-dimensional and structured EO data challenges.

The Foundations pillar supports these applications by advancing core quantum learning techniques. Key contributions include the development of quantum neural network architectures such as quanvolutional layers, quantum MLPs, and quantum RNNs, tailored to extract and process features from EO data. Further work has been dedicated to optimizing these models through improved training methods, quantum-informed hyperparameter tuning, and the use of quantum approaches to accelerate neural architecture search. Collectively, these research efforts contribute to building a cohesive and practical framework that integrates quantum technologies into Earth Observation, positioning QML not only as a theoretical innovation but as a viable tool for advancing geospatial intelligence. For each topic there are one or more publications linked (see below).

Deep Learning for Earth Observation

This summary outlines my research within the AI4EO framework, emphasizing the use of Deep Learning (DL) to address key environmental and societal challenges through Earth Observation (EO) data. The work is structured into three main thematic areas: climate-related, health-related, and emerging applications.

In the climate-related domain, DL models have been developed for monitoring water resources, predicting solar energy potential from EO-based irradiance data, and estimating air pollution levels through satellite-DNN integration. Additional efforts include early prediction of El Niño events using temporal EO data with recurrent networks, and precipitation forecasting through the fusion of EO and DL for improved weather risk assessments. Health-related applications focus on leveraging EO-derived environmental variables to inform public health strategies. This includes analyzing links between air quality, temperature, and Covid-19 trends, and modeling dengue outbreak risks using climate indicators and DL-based epidemiological tools.

Other Projects

Other innovative projects include volcanic eruption monitoring with DL applied to remote sensing data, and the development of on-board AI for real-time processing of EO data on satellites. Overall, this body of work demonstrates how DL can transform EO data into actionable insights, supporting timely decision-making across diverse sectors from climate resilience to public health and space-based intelligence.

Development Tools

• Mainly Python as programming language
• Python libraries for AI: Tensorflow, Pytorch, Tensorboard
• Python libraries for QC: Qiskit, Pennylane
• Self developed libraries for EO and QC4EO: pyosv and hqm

Development Outputs

List of codes and documentations that were developed during my period at ESA Φ-lab:

Publications

Below is a list of publications that were published during my time at ESA Φ-lab:

• Serva, F., Sebastianelli, A., Le Saux, B., & Ricciuti, F. (2025). MUMUCD: A Multimodal Multiclass Change Detection Dataset. IEEE Geoscience and Remote Sensing Letters, 22, 1–5. https://doi.org/10.1109/LGRS.2025.3585797
• Ceschini, A., Carbone, A., Sebastianelli, A., Panella, M., & Le Saux, B. (2025). On hybrid quanvolutional neural networks optimization. Quantum Machine Intelligence, 7(1), 18. https://doi.org/10.1007/s42484-025-00241-z
• Ceschini, A., Mauro, F., Falco, F. D., Sebastianelli, A., Verdone, A., Rosato, A., Saux, B. L., Panella, M., Gamba, P., & Ullo, S. L. (2024). From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks (No. arXiv:2408.06524). arXiv. https://doi.org/10.48550/arXiv.2408.06524
• De Falco, F., Ceschini, A., Sebastianelli, A., Le Saux, B., & Panella, M. (2024). Quantum Hybrid Diffusion Models for Image Synthesis. KI - Künstliche Intelligenz, 38(4), 311–326. https://doi.org/10.1007/s13218-024-00858-5
• De Falco, F., Ceschini, A., Sebastianelli, A., Le Saux, B., & Panella, M. (2024). Quantum latent diffusion models. Quantum Machine Intelligence, 6(2), 85. https://doi.org/10.1007/s42484-024-00224-6
• Del Rosso, M. P., Sebastianelli, A., Spiller, D., Mathieu, P. P., & Ullo, S. L. (2021). On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery. Remote Sensing, 13(17), 3479. https://doi.org/10.3390/rs13173479
• Del Rosso, M. P., Sebastianelli, A., & Ullo, S. L. (Eds.). (2021). Artificial Intelligence Applied to Satellite-based Remote Sensing Data for Earth Observation. Institution of Engineering and Technology. https://doi.org/10.1049/PBTE098E
• Ebel, P., Schneider, R., Bonavita, M., Clare, M., Jungbluth, A., Pourshamsi, M., Chantry, M., Alexe, M., Sebastianelli, A., & Chrust, M. (2024). 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction. Npj Climate and Atmospheric Science, 7(1), 241. https://doi.org/10.1038/s41612-024-00757-4
• Mauro, F., Razzano, F., Stasio, P. D., Sebastianelli, A., Meoni, G., Schirinzi, G., Gamba, P., & Ullo, S. L. (2025). Quantum-Enhanced Water Quality Monitoring: Exploiting $\Phi$ Sat-2 Data With Quanvolution. IEEE Geoscience and Remote Sensing Letters, 22, 1–5. https://doi.org/10.1109/LGRS.2025.3576677
• Mauro, F., Rich, B., Muriga, V. W., Janku, F., Sebastianelli, A., & Ullo, S. L. (2023). SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 297–300. https://doi.org/10.1109/IGARSS52108.2023.10282352
• 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. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 1116–1121. https://doi.org/10.1109/MetroXRAINE58569.2023.10405670
• Mauro, F., Sebastianelli, A., Del Rosso, M. P., Gamba, P., & Ullo, S. L. (2024). Qspecklefilter: A Quantum Machine Learning Approach for SAR Speckle Filtering. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 450–454. https://doi.org/10.1109/IGARSS53475.2024.10642235
• 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 Niño Index (ONI) Prediction. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 812–816. https://doi.org/10.1109/IGARSS53475.2024.10642805
• Mifdal, J., Tomás-Cruz, M., Sebastianelli, A., Coll, B., & Duran, J. (2023). Deep unfolding for hyper sharpening using a high-frequency injection module. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2106–2115. https://doi.org/10.1109/CVPRW59228.2023.00204
• 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. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 4927–4930. https://doi.org/10.1109/IGARSS52108.2023.10282592
• Nowakowski, A., Rosso, M. P. D., Zachar, P., Spiller, D., Gabara, G., Barretta, D., Kalinowska, K. B., Choromański, K., Wilkowski, A., Sebastianelli, A., Kupidura, P., Osińska-Skotak, K., & Ullo, S. L. (2025). Transfer Learning in Earth Observation Data Analysis: A review. IEEE Geoscience and Remote Sensing Magazine, 13(1), 121–152. https://doi.org/10.1109/MGRS.2024.3494673
• Papa, L., Sebastianelli, A., Meoni, G., & Amerini, I. (2024). On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation (No. arXiv:2410.08677). arXiv. https://doi.org/10.48550/arXiv.2410.08677
• Reale, S., Stasio, P. D., Mauro, F., Sebastianelli, A., Gamba, P., & Ullo, S. L. (2024). Benchmarking of a new data splitting method on volcanic eruption data (No. arXiv:2410.06306). arXiv. https://doi.org/10.48550/arXiv.2410.06306
• Rosso, M. P. D., Sebastianelli, A., Spiller, D., & Ullo, S. L. (2022). A demo setup testing onboard CNNs for Volcanic Eruption Detection. 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 719–724. https://doi.org/10.1109/MetroXRAINE54828.2022.9967684
• 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. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 1922–1926. https://doi.org/10.1109/IGARSS53475.2024.10641660
• Russo, L., Mauro, F., Sebastianelli, A., Gamba, P., & Ullo, S. L. (2024). SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking (No. arXiv:2409.17087). arXiv. https://doi.org/10.48550/arXiv.2409.17087
• Schneider, R., Sebastianelli, A., Spiller, D., Wheeler, J., Carmo, R., Nowakowski, A., Garcia-Herranz, M., Kim, D., Barlevi, H., & Cordero, Z. E. R. (2021). Climate-based ensemble machine learning model to forecast Dengue epidemics (papers track). Thirty-eighth International Conference on Machine Learning (ICML) 2021.
• Sebastianelli, A., Mauro, F., Ciabatti, G., Spiller, D., Le Saux, B., Gamba, P., & Liberata Ullo, S. (2025). Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 63, 1–15. https://doi.org/10.1109/TGRS.2025.3556335
• Sebastianelli, A., Puglisi, E., Del Rosso, M. P., Mifdal, J., Nowakowski, A., Mathieu, P. P., Pirri, F., & Ullo, S. L. (2022). PLFM: Pixel-Level Merging of Intermediate Feature Maps by Disentangling and Fusing Spatial and Temporal Data for Cloud Removal. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16. https://doi.org/10.1109/TGRS.2022.3208694
• Sebastianelli, A., Rosso, M. P. D., Ullo, S. L., & Gamba, P. (2022). A Speckle Filter for Sentinel-1 SAR Ground Range Detected Data Based on Residual Convolutional Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5086–5101. https://doi.org/10.1109/JSTARS.2022.3184355
• Sebastianelli, A., Rosso, M. P. D., Ullo, S. L., & Gamba, P. (2023). On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data. IEEE Geoscience and Remote Sensing Letters, 20, 1–5. https://doi.org/10.1109/LGRS.2023.3308105
• Sebastianelli, A., Serva, F., Ceschini, A., Paletta, Q., Panella, M., & Le Saux, B. (2024). Machine learning forecast of surface solar irradiance from meteo satellite data. Remote Sensing of Environment, 315, 114431. https://doi.org/10.1016/j.rse.2024.114431
• Sebastianelli, A., Spiller, D., Carmo, R., Wheeler, J., Nowakowski, A., Jacobson, L. V., Kim, D., Barlevi, H., Cordero, Z. E. R., Colón-González, F. J., Lowe, R., Ullo, S. L., & Schneider, R. (2024). A reproducible ensemble machine learning approach to forecast dengue outbreaks. Scientific Reports, 14(1), 3807. https://doi.org/10.1038/s41598-024-52796-9
• Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022). On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 565–580. https://doi.org/10.1109/JSTARS.2021.3134785
• Spiller, D., Santin, G., Sebastianelli, A., Lucchini, L., Gallotti, R., Lake, B., Ullo, S. L., Saux, B. L., & Lepri, B. (2023). Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables (No. arXiv:2302.14649). arXiv. https://doi.org/10.48550/arXiv.2302.14649
• Stasio, P. D., Sebastianelli, A., Meoni, G., & Ullo, S. L. (2022). Early Detection of Volcanic Eruption through Artificial Intelligence on board. 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 714–718. https://doi.org/10.1109/MetroXRAINE54828.2022.9967616
• Zaidenberg, D. A., Sebastianelli, A., Spiller, D., Le Saux, B., & Ullo, S. L. (2021). Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 5680–5683. https://doi.org/10.1109/IGARSS47720.2021.9553133