Articles | Volume 21, issue 10
https://doi.org/10.5194/nhess-21-3199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-3199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Oct 2021
Research article |
| 27 Oct 2021
| 27 Oct 2021
Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi
Lucas Wouters
CORRESPONDING AUTHOR
Institute for Environmental studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, the Netherlands
510, an initiative of the Netherlands Red Cross, Anna van Saksenlaan 50, 2593 HT Den Haag, the Netherlands
Anaïs Couasnon
Institute for Environmental studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, the Netherlands
Marleen C. de Ruiter
Institute for Environmental studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, the Netherlands
Marc J. C. van den Homberg
510, an initiative of the Netherlands Red Cross, Anna van Saksenlaan 50, 2593 HT Den Haag, the Netherlands
Aklilu Teklesadik
510, an initiative of the Netherlands Red Cross, Anna van Saksenlaan 50, 2593 HT Den Haag, the Netherlands
Hans de Moel
Institute for Environmental studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, the Netherlands
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Cited
14 citations as recorded by crossref.
- Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
- Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview M. de Brito et al. 10.1029/2023EF003906
- Post-Flood Analysis for Damage and Restoration Assessment Using Drone Imagery D. Whitehurst et al. 10.3390/rs14194952
- Flood Extent Delineation and Exposure Assessment in Senegal Using the Google Earth Engine: The 2022 Event B. Sy et al. 10.3390/w16152201
- Flood vulnerability assessment of buildings using geospatial data and machine learning classifiers T. Tam et al. 10.1007/s11600-024-01527-9
- Long-term tracking of recovery of built infrastructure after wildfires with deep network topologies A. Schmidt et al. 10.1007/s00521-025-11003-0
- Quantifying war-induced crop losses in Ukraine in near real time to strengthen local and global food security K. Deininger et al. 10.1016/j.foodpol.2023.102418
- Precise LULC classification of rural area combining elevational and reflectance characteristics using UAV K. Zhang et al. 10.1016/j.sciaf.2024.e02431
- Emerging strategies for addressing flood-damage modeling issues: A review S. Redondo-Tilano et al. 10.1016/j.ijdrr.2024.105058
- Flood risk assessment and participative process in the data-scarce Metuge district of Mozambique: An exportable approach S. Rrokaj et al. 10.1016/j.ijdrr.2024.105163
- Road extraction in diverse urban environments using UAV data and nDSM perturbations: A case of Bhopal, India A. Dabra et al. 10.1016/j.rsase.2025.101465
- Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet Y. Xu et al. 10.3390/app12199484
- Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases B. Masinde et al. 10.3390/ijgi13120419
- Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood W. Li et al. 10.3390/hydrology10010017
14 citations as recorded by crossref.
- Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
- Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview M. de Brito et al. 10.1029/2023EF003906
- Post-Flood Analysis for Damage and Restoration Assessment Using Drone Imagery D. Whitehurst et al. 10.3390/rs14194952
- Flood Extent Delineation and Exposure Assessment in Senegal Using the Google Earth Engine: The 2022 Event B. Sy et al. 10.3390/w16152201
- Flood vulnerability assessment of buildings using geospatial data and machine learning classifiers T. Tam et al. 10.1007/s11600-024-01527-9
- Long-term tracking of recovery of built infrastructure after wildfires with deep network topologies A. Schmidt et al. 10.1007/s00521-025-11003-0
- Quantifying war-induced crop losses in Ukraine in near real time to strengthen local and global food security K. Deininger et al. 10.1016/j.foodpol.2023.102418
- Precise LULC classification of rural area combining elevational and reflectance characteristics using UAV K. Zhang et al. 10.1016/j.sciaf.2024.e02431
- Emerging strategies for addressing flood-damage modeling issues: A review S. Redondo-Tilano et al. 10.1016/j.ijdrr.2024.105058
- Flood risk assessment and participative process in the data-scarce Metuge district of Mozambique: An exportable approach S. Rrokaj et al. 10.1016/j.ijdrr.2024.105163
- Road extraction in diverse urban environments using UAV data and nDSM perturbations: A case of Bhopal, India A. Dabra et al. 10.1016/j.rsase.2025.101465
- Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet Y. Xu et al. 10.3390/app12199484
- Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases B. Masinde et al. 10.3390/ijgi13120419
- Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood W. Li et al. 10.3390/hydrology10010017
Latest update: 12 Feb 2025
Short summary
This research introduces a novel approach to estimate flood damage in Malawi by applying a machine learning model to UAV imagery. We think that the development of such a model is an essential step to enable the swift allocation of resources for recovery by humanitarian decision-makers. By comparing this method (EUR 10 140) to a conventional land-use-based approach (EUR 15 782) for a specific flood event, recommendations are made for future assessments.
This research introduces a novel approach to estimate flood damage in Malawi by applying a...
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