awesome-QC4EO

2024

APA Original Link arXiv or similar
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. arXiv preprint arXiv:2401.16049. link link
Mauro, F., Sebastianelli, A., Del Rosso, M. P., Gamba, P., & Ullo, S. L. (2024). QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering. arXiv preprint arXiv:2402.01235. link link
Pasetto, E., Riedel, M., Michielsen, K., & Cavallaro, G. (2024). Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. link link
Malarvanan, A. S. (2024). Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio. arXiv preprint arXiv:2403.02080. link link

2023

APA Original Link arXiv or similar
Ullo, S. L., Mauro, F., Sebastianelli, A., Le Saux, B., & Gamba, P. E. (2024). Enhancing Earth Observation with Hybrid Quantum Neural Networks. AGU23. link link
Sebastianelli, A., Del Rosso, M. P., Ullo, S. L., & Gamba, P. (2023). On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data. IEEE Geoscience and Remote Sensing Letters. link link
Delilbasic, A., Le Saux, B., Riedel, M., Michielsen, K., & Cavallaro, G. (2023). A single-step multiclass svm based on quantum annealing for remote sensing data classification. IEEE journal of selected topics in applied earth observations and remote sensing. link link
Miroszewski, A., Nalepa, J., Saux, B. L., & Mielczarek, J. (2023). Quantum Machine Learning for Remote Sensing: Exploring potential and challenges. arXiv preprint arXiv:2311.07626. link link
Gupta, M. K., Romaszewski, M., & Gawron, P. (2023). Potential of quantum machine learning for processing multispectral Earth observation data. Authorea Preprints. link link
Chang, S. Y., Grossi, M., Le Saux, B., & Vallecorsa, S. (2023, September). Approximately equivariant quantum neural network for p4m group symmetries in images. In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 229-235). IEEE. link link
Miroszewski, A., Mielczarek, J., Czelusta, G., Szczepanek, F., Grabowski, B., Le Saux, B., & Nalepa, J. (2023). Detecting clouds in multispectral satellite images using quantum-kernel support vector machines. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. link link
Miroszewski, A., Mielczarek, J., Szczepanek, F., Czelusta, G., Grabowski, B., Le Saux, B., & Nalepa, J. (2023, July). Cloud Detection in Multispectral Satellite Images Using Support Vector Machines with Quantum Kernels. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 796-799). IEEE. link link
Fan, F., Shi, Y., & Zhu, X. X. (2023, May). Urban land cover classification from Sentinel-2 images with quantum-classical network. In 2023 Joint Urban Remote Sensing Event (JURSE) (pp. 1-4). IEEE. link link
Nammouchi, A., Kassler, A., & Theocharis, A. (2023). Quantum Machine Learning in Climate Change and Sustainability: A Short Review. In Proceedings of the AAAI Symposium Series (Vol. 2, No. 1, pp. 107-114). link link
Otgonbaatar, S., & Kranzlmüller, D. (2023). Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment. IEEE Transactions on Quantum Engineering. link link
Otgonbaatar, S., Schwarz, G., Datcu, M., & Kranzlmüller, D. (2023). Quantum transfer learning for real-world, small, and high-dimensional remotely sensed datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. link link
Miller, L., Uehara, G., Sharma, A., & Spanias, A. (2023, June). Quantum Machine Learning for Optical and SAR Classification. In 2023 24th International Conference on Digital Signal Processing (DSP) (pp. 1-5). IEEE. link link

2022

APA Original Link arXiv or similar
Chang, S. Y., Le Saux, B., Vallecorsa, S., & Grossi, M. (2022, July). Quantum convolutional circuits for earth observation image classification. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 4907-4910). IEEE. link link
Fan, F., Shi, Y., & Zhu, X. X. (2022, July). Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 191-194). IEEE. link link
Otgonbaatar, S., Datcu, M., Zhu, X. X., & Kranzlmüller, D. (2022). Quantum Machine Learning for Real-World, Large Scale Datasets with Applications in Earth Observation. link link
Pasetto, E., Delilbasic, A., Cavallaro, G., Willsch, M., Melgani, F., Riedel, M., & Michielsen, K. (2022, July). Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 4903-4906). IEEE. link link
Shaik, R. U., & Periasamy, S. (2022). Accuracy and processing speed trade-offs in classical and quantum SVM classifier exploiting PRISMA hyperspectral imagery. International Journal of Remote Sensing, 43(15-16), 6176-6194. link link

2021

APA Original Link arXiv or similar
Zaidenberg, D. A., Sebastianelli, A., Spiller, D., Le Saux, B., & Ullo, S. L. (2021, July). Advantages and bottlenecks of quantum machine learning for remote sensing. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 5680-5683). IEEE. link link
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. L. (2021). 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. link link
Otgonbaatar, S., & Datcu, M. (2021). Assembly of a coreset of earth observation images on a small quantum computer. Electronics, 10(20), 2482. link link

Unclassified

APA Original Link arXiv or similar
Painchart, H., van Waveren, M., & Mora, B. QUANTUM ALGORITHM FOR THE ANALYSIS OF TEMPORAL SEQUENCES OF SATELLITE IMAGES. link link
Matthijs van Waveren, C. S. On the use of a quantum convolutional neural network for remote sensing imagery classification. link link
Defonte, V., van Waveren, M., Pasero, G., Savinaud, M., Gawron, P., Brunet, P. M., … & PAS, R. QUANTUM CONTRASTIVE LEARNING FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES. link link