UAV imagery in natural disasters: Real-time damage assessment of flash flooding events

[Data availability] Following the recommended practices for advancing research in Ecological Informatics, as outlined by Huettmann and Arhonditsis (2023), the data and code used in this study has been made publicly available to ensure full reproducibility. The code employed for object detection (veh...

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Detalhes bibliográficos
Autores: Román, Alejandro, Tovar-Sánchez, Antonio, Larrad, Marcos, Rubiano, Francisco, Zafra González, José Manuel, Piñeiro Martínez de Lecea, Rafael, Castillo Talavera, Ángel, López Gómez, Félix Antonio, Lucía Vela, Ana, Allende, Ana, Sánchez, Gloria, Martínez-Alonso, Alberto, Samper, Daniel, García López-Davalillo, Juan Carlos, Galindo Jiménez, Inés, Navarro, Gabriel
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/401065
Acesso em linha:http://hdl.handle.net/10261/401065
https://api.elsevier.com/content/abstract/scopus_id/105016219627
Access Level:Acceso aberto
Palavra-chave:Artificial intelligence
Drone
Emergency
LiDAR
Natural disaster
Descrição
Resumo:[Data availability] Following the recommended practices for advancing research in Ecological Informatics, as outlined by Huettmann and Arhonditsis (2023), the data and code used in this study has been made publicly available to ensure full reproducibility. The code employed for object detection (vehicles and trash items) using the YOLOv11 architecture can be downloaded from: https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb. Regarding the UAV data, the LiDAR data used for flooding forecasting are available at: http://hdl.handle.net/10261/372062. The remaining datasets are available for download at: http://hdl.handle.net/10261/398044

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