Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence
Urban flood risk mitigation requires fine-scale near-real-time precipitation observations that are challenging to obtain from traditional monitoring networks. Novel data and computational techniques offer a valuable potential source of information. This study explores an unprecedented, device-indepe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Environmental Sciences Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4931/21/1/35 |
| _version_ | 1797580191712673792 |
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| author | Nicla Maria Notarangelo Kohin Hirano Raffaele Albano Aurelia Sole |
| author_facet | Nicla Maria Notarangelo Kohin Hirano Raffaele Albano Aurelia Sole |
| author_sort | Nicla Maria Notarangelo |
| collection | DOAJ |
| description | Urban flood risk mitigation requires fine-scale near-real-time precipitation observations that are challenging to obtain from traditional monitoring networks. Novel data and computational techniques offer a valuable potential source of information. This study explores an unprecedented, device-independent, artificial intelligence-based system for opportunistic rainfall monitoring through deep learning models that detect rainfall presence and estimate quasi-instantaneous intensity from single pictures. Preliminary results demonstrate the models’ ability to detect a significant meteorological state corroborating the potential of non-dedicated sensors for hydrometeorological monitoring in urban areas and data-scarce regions. Future research will involve further experiments and crowdsourcing, to improve accuracy and promote public resilience. |
| first_indexed | 2024-03-10T22:47:52Z |
| format | Article |
| id | doaj.art-fe14fde36a8f41a485bf8871058a5244 |
| institution | Directory Open Access Journal |
| issn | 2673-4931 |
| language | English |
| last_indexed | 2024-03-10T22:47:52Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environmental Sciences Proceedings |
| spelling | doaj.art-fe14fde36a8f41a485bf8871058a52442023-11-19T10:37:15ZengMDPI AGEnvironmental Sciences Proceedings2673-49312022-10-012113510.3390/environsciproc2022021035Opportunistic Rainfall Monitoring from Single Pictures Using Artificial IntelligenceNicla Maria Notarangelo0Kohin Hirano1Raffaele Albano2Aurelia Sole3School of Engineering, University of Basilicata, 85100 Potenza, ItalyStorm, Flood and Landslide Research Division, National Research Institute for Earth Science and Disaster Resilience NIED, Tuskuba 305-0006, JapanSchool of Engineering, University of Basilicata, 85100 Potenza, ItalySchool of Engineering, University of Basilicata, 85100 Potenza, ItalyUrban flood risk mitigation requires fine-scale near-real-time precipitation observations that are challenging to obtain from traditional monitoring networks. Novel data and computational techniques offer a valuable potential source of information. This study explores an unprecedented, device-independent, artificial intelligence-based system for opportunistic rainfall monitoring through deep learning models that detect rainfall presence and estimate quasi-instantaneous intensity from single pictures. Preliminary results demonstrate the models’ ability to detect a significant meteorological state corroborating the potential of non-dedicated sensors for hydrometeorological monitoring in urban areas and data-scarce regions. Future research will involve further experiments and crowdsourcing, to improve accuracy and promote public resilience.https://www.mdpi.com/2673-4931/21/1/35opportunistic rainfall monitoringcamera-based rainfall monitoringartificial intelligencedeep learningconvolutional neural networkssingle image classification |
| spellingShingle | Nicla Maria Notarangelo Kohin Hirano Raffaele Albano Aurelia Sole Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence Environmental Sciences Proceedings opportunistic rainfall monitoring camera-based rainfall monitoring artificial intelligence deep learning convolutional neural networks single image classification |
| title | Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence |
| title_full | Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence |
| title_fullStr | Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence |
| title_full_unstemmed | Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence |
| title_short | Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence |
| title_sort | opportunistic rainfall monitoring from single pictures using artificial intelligence |
| topic | opportunistic rainfall monitoring camera-based rainfall monitoring artificial intelligence deep learning convolutional neural networks single image classification |
| url | https://www.mdpi.com/2673-4931/21/1/35 |
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