Fig. 1
Risky situations example.
Table 1
Examples of geo-data possible to get with UAVs (drones). Adapted from Gevaert et al. 2018.
| Acquired directly from drone footage | Indirectly acquired from drone footage | Not captured with the drone – need for other sources |
|---|---|---|
| Road System | Land Use/Cover | Population counts and another census information |
| Building boundaries | Waste dumping sites | Income, etc. |
| Vegetation | Urban infrastructure | Administrative boundaries |
| Surface models (DSM) | Contextual Information | Security of tenure |
| Terrain models (DTM) | Attributes of the features, such as: roof type, number of floors, building material, etc. | Temperature of materials and surfaces (not with conventional cameras) |
[i] UAVs – unmanned aerial vehicles.
Fig. 2
Salvador de Bahia and the Alto das Pombas Census Sector.
Fig. 3
3D model of the study generated by drone: Alto das Pombas Community.
Fig. 4
Terrain slope below the houses in the study area of Alto das Pombas, in Salvador/Bahia.
Fig. 5
The zonal mean calculation for slope and building height.
Fig. 6
Spatial patterns of buildings distributed by the number of floors, Alto das Pombas Salvador/Bahia.
Fig. 7
3D model with buildings distributed by the number of floors.
Fig. 8
Google Earth a and drone b. Detailing the spacing between buildings, and roof cover type.
Fig. 9
Google Earth a drone b. Unprotected stairways and slabs became visible.
Fig. 10
Identification of dwellings been enlarged and with apparent unprotected slabs/stairs.
Table 2
Drone collected data during the flight above the census sector in Alto das Pombas, Salvador/Bahia.
| Physical characteristics of the dwellings | Potentials and limitations of the drone imagery |
|---|---|
| Buildings’ land slopes | Using the terrain model, generated from the image processing, a map was prepared that indicates the slope of the terrain where each building is located. Information is available on Figure 4. |
| Building floors | From the terrain and surface models, it was possible to identify the approximate number of floors of each dwelling. Information is available on Figure 6. |
| Spacing between buildings | It is possible to identify and measure approximate spacing between buildings, as shown in Figures 8. |
| Unprotected exposed slabs and stairs | It was possible to identify the dwellings that visually presented these characteristics. Information is present in Figure 9. |
| Width of internal roads | It is possible to delineate the width and type of internal roads (whether staircases or not), the type of paving, as per examples in Figures 3 and 4. |
| Type of roof covering | It is possible to identify the type of roof coverage of the buildings, as per examples in Figures 8 and 9. |
| Dwelling in expansion | It was possible to identify the dwellings that were visually enlarging. Information is present in Figure 10. |
| External coating of the dwellings | It was not possible to clearly identify the type of covering of the buildings. It is suggested to conduct new flights with different altitudes, and different camera angles on the drone. |