{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T21:46:30Z","timestamp":1783460790856,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T00:00:00Z","timestamp":1696636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Khalifa University and the SmartSat Cooperative Research Centre (CRC)","award":["FSU-2022-013"],"award-info":[{"award-number":["FSU-2022-013"]}]},{"name":"Khalifa University and the SmartSat Cooperative Research Centre (CRC)","award":["2.13s"],"award-info":[{"award-number":["2.13s"]}]},{"name":"Doctoral Research Project","award":["FSU-2022-013"],"award-info":[{"award-number":["FSU-2022-013"]}]},{"name":"Doctoral Research Project","award":["2.13s"],"award-info":[{"award-number":["2.13s"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model\u2019s performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires.<\/jats:p>","DOI":"10.3390\/rs15194855","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T04:52:36Z","timestamp":1696827156000},"page":"4855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-3187","authenticated-orcid":false,"given":"Dario","family":"Spiller","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy"},{"name":"Sir Lawrence Wackett Defence and Aerospace Centre, RMIT University, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1119-060X","authenticated-orcid":false,"given":"Andrea","family":"Carbone","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2410-646X","authenticated-orcid":false,"given":"Stefania","family":"Amici","sequence":"additional","affiliation":[{"name":"National Institute of Geophysics and Volcanology (INGV), 00143 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3401-9493","authenticated-orcid":false,"given":"Kathiravan","family":"Thangavel","sequence":"additional","affiliation":[{"name":"Sir Lawrence Wackett Defence and Aerospace Centre, RMIT University, Melbourne, VIC 3000, Australia"},{"name":"School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy"},{"name":"SmartSat Cooperative Research Centre, North Terrace, Adelaide, SA 5000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3399-2291","authenticated-orcid":false,"given":"Roberto","family":"Sabatini","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Sir Lawrence Wackett Defence and Aerospace Centre, RMIT University, Melbourne, VIC 3000, Australia"},{"name":"SmartSat Cooperative Research Centre, North Terrace, Adelaide, SA 5000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6108-9764","authenticated-orcid":false,"given":"Giovanni","family":"Laneve","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"key":"ref_1","unstructured":"Lollino, G., Manconi, A., Guzzetti, F., Culshaw, M., Bobrowsky, P., and Luino, F. 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