{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:49:23Z","timestamp":1778600963078,"version":"3.51.4"},"reference-count":83,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T00:00:00Z","timestamp":1725667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT)","award":["2022.12886.BD"],"award-info":[{"award-number":["2022.12886.BD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap\u2019s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.<\/jats:p>","DOI":"10.3390\/ijgi13090323","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T04:15:01Z","timestamp":1725855301000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Mapping Geospatial AI Flood Risk in National Road Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2257-5361","authenticated-orcid":false,"given":"Seyed M. H. S.","family":"Rezvani","sequence":"first","affiliation":[{"name":"Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior T\u00e9cnico, Universidade de Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3723-0948","authenticated-orcid":false,"given":"Maria Jo\u00e3o Falc\u00e3o","family":"Silva","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio Nacional de Engenharia Civil, Av. do Brasil 101, 1700-075 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-2679","authenticated-orcid":false,"given":"Nuno Marques de","family":"Almeida","sequence":"additional","affiliation":[{"name":"Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior T\u00e9cnico, Universidade de Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,7]]},"reference":[{"key":"ref_1","first-page":"71","article-title":"GeoAI: Where Machine Learning and Big Data Converge in GIScience","volume":"20","author":"Li","year":"2020","journal-title":"J. Spat. Inf. 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