Flood warning | Evacuation warning | UAS with an embedded audible alarm to provide an alert about an upcoming flood and the need for evacuation (Mozambique). | N/A | N/A | Real-time | Flight path reach |
Flood monitoring and flood risk assessment | Visualization of flood extent | Delineation of inundated areas by digitizing the boundaries at the contrasting land surface/water boundary [28]. | RGB imagery RGB video | Flood extent, Ponding locations | Real time (<1 h) | 0.2 m high resolution RGB imagery 4 |
Flood extent detection | Application of an algorithm to detect flood areas automatically [44,45]. | RGB imagery | Orthoimage, flood extent | >48h 1 | 0.2 m high resolution RGB imagery 4 |
A spectral difference index is generated from the RGB photos to map flood water extent [46]. | RGB imagery | Orthoimage, flood extent | Real time (<1 h) | 0.2 m high resolution in RGB imagery |
Modelling flood extent | A concept of transfer learning where a Convolutional Neural Network model is trained based on one dataset, transferred and used to classify another dataset to delineate flood extent [47]. | RGB imagery | Orthoimage, DEM, flood extent | <48 h | 1.5 cm ultra-high resolution in RGB imagery with 93% accuracy in flood extent |
A high accuracy terrain model combined with hydraulic calculations performed on transverse profiles produce the flood-prone areas at 1% and 5% exceedance probabilities of discharge [48]. |
A DEM mapped with UAS is used in hydrologic and hydraulic modelling to provide a flood hazard map [49]. |
Point measurement of flow velocity | A set of floating wireless sensors are deployed within the flood extent by multiple UAS to capture water velocity readings at multiple locations [50]. | Water velocity readings. Infrared imaging | Water velocity | Real time (<1 h) | Estimated 0.15 m/s accuracy 5 |
A combination of floating, infrared light-emitting particles and a programmable embedded colour vision sensor to simultaneously detect and track objects [51]. |
Optical water velocity | Bespoke algorithms are used to track the movement of tracers [52], or texture [53,54,55] in the water surface in consecutive frames obtained from video footage. | RGB imagery RGB video streaming HD video | Orthoimage, water velocity | >48 h 1 | Estimated 0.5 m/s accuracy 5 |
Water velocity | Real time (<1 h) |
Visual flood depth | Flood depth is estimated through the observation of wrack marks (post-event) [28] or via the observation of the water level (during event) against know height points (e.g., cars, bridges, feature buildings). | (Oblique) RGB imagery RGB video | Flood depth | Real time (<1 h) | High resolution RGB imagery |
Point measured of flood depth | UAS with ultrasonic sensors used to detect water level [56]. A pre-event DEM is required to estimate flood depth. | Water depth readings | Water level, point cloud, DEM, DTM, DSM | <48 h 2 | 6 cm accuracy |
Flood source identification (fluvial, pluvial, groundwater) | Sources of flooding can be identified based on damaged caused within/outside the fluvial flood extent [15,57] or based on differences in water temperature [58]. | RGB frames Thermal frames | Orthoimage, DEM, DTM, DSM | >48 h | 1 m resolution (DEM) |
Evacuation routes identification | Modelled evacuation route identification | Modelling of evacuation routes by using UAS as end devices of M2M architecture [59]. The input model needs DEM, DTM and/or DSM as well as hydrological/hydraulic input data. | RGB imagery RGB video | Map of evacuation routes, DSM, DTM, DEM, orthoimage | <24 h | 20–60% tracking accuracy |
Surface changes and displacements of landslides | Surface changes and displacements of landslides [60,61]. | RGB imagery RGB video | 3D point clouds, DEM, orthoimage | <48 h 2 | 1 cm ultra-high resolution (DEM), accuracy: 7.4 cm (horizontal) × 6.2 cm (vertical) |
Damage assessment | Visual detection of affected properties, businesses, hospitals, schools | To collect information on damage to hospitals for patient rescue and for efficient allocation of resources [62]. | RGB imagery | Location of affected properties | Real time (<1 h) | Resolution at building level |
Resistance and resilience measures identification | Identification of residential properties with resistance measures (i.e., flood aperture guards for doors and windows, flood resistant airbricks, and raised doors or steps leading to a property) [15]. | RGB imagery | Orthoimage, DEM | >48 h | Resolution at building level |
Rescue | Identification of safe shelter points | To identify where to best place NGO camps [21] and to identify land that could be safer to relocate families [20]. | RGB imagery RGB video | Map with location of points | Real time (<1 h) | Resolution at building level |
Detection of stranded people | The use of UAS to locate stranded people [63,64] even at night [65,66] and specifically during floods [67]. | RGB imagery RGB video Infrared imaging | N/A | Real time (<1 h) | Resolution at individual level |
Delivery of ad-hoc supplies | The use of UAS to deliver equipment or resources that guarantee the survival of stranded people e.g. to carry a radio to communicate [64], floating devices [65]. | RGB imagery RGB video | N/A | Real time (<1 h) | Resolution at individual level |