Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development
Accessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not ava...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/9/1739 |
| _version_ | 1797535654818611200 |
|---|---|
| author | Daniel Whitehurst Brianna Friedman Kevin Kochersberger Venkat Sridhar James Weeks |
| author_facet | Daniel Whitehurst Brianna Friedman Kevin Kochersberger Venkat Sridhar James Weeks |
| author_sort | Daniel Whitehurst |
| collection | DOAJ |
| description | Accessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not available to or affordable for resource-limited communities. In this paper, we present a concept of aerial data collection, 3D cadastre modeling, and disaster risk assessment using low-cost drones and adapted open-source software. Computer vision/machine learning methods are used to create a classified 3D cadastre that contextualizes and quantifies potential natural disaster risk to existing or planned infrastructure. Building type and integrity are determined from aerial imagery. Potential flood damage risk to a building is evaluated as a function of three mechanisms: undermining (erosion) of the foundation, hydraulic pressure damage, and building collapse due to water load. Use of Soil and Water Assessment Tool (SWAT) provides water runoff estimates that are improved using classified land features (urban ecology, erosion marks) to improve flow direction estimates. A convolutional neural network (CNN) is trained to find these flood-induced erosion marks from high-resolution drone imagery. A flood damage potential metric scaled by property value estimates results in individual and community property risk assessments. |
| first_indexed | 2024-03-10T11:48:26Z |
| format | Article |
| id | doaj.art-b218df743fa8440881f9b5a2e0b87492 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:48:26Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b218df743fa8440881f9b5a2e0b874922023-11-21T17:54:28ZengMDPI AGRemote Sensing2072-42922021-04-01139173910.3390/rs13091739Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable DevelopmentDaniel Whitehurst0Brianna Friedman1Kevin Kochersberger2Venkat Sridhar3James Weeks4Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USAMechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USAMechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USABiological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USADevelopment Monitors, LLC, Arlington, VA 22202, USAAccessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not available to or affordable for resource-limited communities. In this paper, we present a concept of aerial data collection, 3D cadastre modeling, and disaster risk assessment using low-cost drones and adapted open-source software. Computer vision/machine learning methods are used to create a classified 3D cadastre that contextualizes and quantifies potential natural disaster risk to existing or planned infrastructure. Building type and integrity are determined from aerial imagery. Potential flood damage risk to a building is evaluated as a function of three mechanisms: undermining (erosion) of the foundation, hydraulic pressure damage, and building collapse due to water load. Use of Soil and Water Assessment Tool (SWAT) provides water runoff estimates that are improved using classified land features (urban ecology, erosion marks) to improve flow direction estimates. A convolutional neural network (CNN) is trained to find these flood-induced erosion marks from high-resolution drone imagery. A flood damage potential metric scaled by property value estimates results in individual and community property risk assessments.https://www.mdpi.com/2072-4292/13/9/1739droneaerial imagerydisaster risk managementclassification3D modeling |
| spellingShingle | Daniel Whitehurst Brianna Friedman Kevin Kochersberger Venkat Sridhar James Weeks Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development Remote Sensing drone aerial imagery disaster risk management classification 3D modeling |
| title | Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development |
| title_full | Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development |
| title_fullStr | Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development |
| title_full_unstemmed | Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development |
| title_short | Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development |
| title_sort | drone based community assessment planning and disaster risk management for sustainable development |
| topic | drone aerial imagery disaster risk management classification 3D modeling |
| url | https://www.mdpi.com/2072-4292/13/9/1739 |
| work_keys_str_mv | AT danielwhitehurst dronebasedcommunityassessmentplanninganddisasterriskmanagementforsustainabledevelopment AT briannafriedman dronebasedcommunityassessmentplanninganddisasterriskmanagementforsustainabledevelopment AT kevinkochersberger dronebasedcommunityassessmentplanninganddisasterriskmanagementforsustainabledevelopment AT venkatsridhar dronebasedcommunityassessmentplanninganddisasterriskmanagementforsustainabledevelopment AT jamesweeks dronebasedcommunityassessmentplanninganddisasterriskmanagementforsustainabledevelopment |