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Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model’s qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanc, We would like to thank the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and IRCAI (International Research Centre on Artificial Intelligence) for recognising this project as one of the Global TOP 100 AI solutions for sustainable development goals (SDGs). We also thank UNICEF Innocenti for selecting our project as one of the Best of UNICEF Research for 2022. Rachel Lowe acknowledges support from a Royal Society Dorothy Hodgkin Fellowship and the Wellcome Trust grant number 224694/Z/21/Z (HARMONIZE). The authors would like to thank Manuel García-Herranz from UNICEF for participation and contribution at the early stage of this project., Peer Reviewed, "Article signat per 13 autors/es: Alessandro Sebastianelli, Dario Spiller, Raquel Carmo, James Wheeler, Artur Nowakowski, Ludmilla Viana Jacobson, Dohyung Kim, Hanoch Barlevi, Zoraya El Raiss Cordero, Felipe J Colón-González, Rachel Lowe, Silvia Liberata Ullo & Rochelle Schneide", Postprint (published version)

This paper shows the application of autonomous Crater Detection using the U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on optical images of the Moon Global Morphology Mosaic based on data collected by the LRO and manual crater catalogues. The Moon-trained network will be tested on Dawn optical images of Ceres: this task is accomplished by means of a Transfer Learning (TL) approach. The trained model has been fine-tuned using 100, 500 and 1000 additional images of Ceres. The test performance was measured on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95% and 97.19%, respectively. This means that despite the intrinsic differences between the Moon and Ceres, TL works with encouraging results. The output of the U-Net contains predicted craters: it will be post-processed applying global thresholding for image binarization and a template matching algorithm to extract craters positions and radii in the pixel space. Post-processed craters will be counted and compared to the ground truth data in order to compute image segmentation metrics: precision, recall and F1 score. These indices will be computed, and their effect will be discussed for tasks such as automated crater cataloguing and optical navigation.

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case, and tested on the EuroSAT dataset used as reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations., Comment: Submitted to the JSTARS special issue on "Quantum resources for Earth Observation" for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study and a first prototype for an Artificial Intelligence (AI) model to be deployed on board satellites are presented in this work. As a case study, the detection of volcanic eruptions has been investigated as a method to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been proposed and designed, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements.

This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options., Comment: Submitted and accepted for IEEE IGARSS2021