# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Case Study
#### 2.2.2 UAS Flight Data and Fusion  
A DJI Phantom 4 Pro was used with DroneDeploy [44] software to acquire aerial imagery in the area identified as a high priority for DEM update. The flight included areas outside the bounds of the priority queue to ensure sufficient overlap with the unchanged  
Figure 4: The spatial distributions of land cover samples from OSM inside of Walnut Creek. Figure 3: The datasets used for the case study were generated over a wide variety of spatial–temporal scales. area and to represent the entire planned construction area, which may be used in future studies involving the site. Ground control points were not collected or used in processing because the busy construction site was inaccessible to the public. The Phantom 4 Pro has a max flight time of 30 minutes, uses a 20-megapixel RGB sensor and a GPS and GLONASS (Globalnaya Navigatsionnaya Sputnikovaya Sistema) GNSS receiver. It has a ground sampling distance of 1.91 cm/px at 70 m altitude, a GNSS vertical accuracy of \(\pm\)0.5 m and horizontal accuracy of \(\pm\)1.5 m [46]. The survey was autonomously flown at 116 m above the ground altitude in a linear pattern to reduce flight time. Front and side overlaps were set to 75% and 65% each and the gimbal angle set to 90 degrees, nadir, manual focus set to infinity, ISO set to auto, and shutter priority mode set to 1/800 s. During the 25 min flight, 322 images were collected and covered 0.63 km\({}^{2}\) of the area. The resulting UAS data was processed in WebDM using customized parameters (Table A2) to improve the accuracy of the resulting DEMs [24]. The processed data included a 15 cm resolution DTM, DSM, and orthoimagery with an average ground sampling distance of 6.2 cm. A full flight report is available in the Supplementary Materials (Report S1). The UAS DTM generated from WebDM was not a true DTM because vegetation and buildings were only partially removed. To account for this the DSM was further processed to generate a smoothed (i.e., 5-pixel moving window mean) bare ground DEM (UAS DEM)(Figure A1). The UAS DEM was fused with a DEM from the United State Geological Survey (USGS) National Elevation Dataset (NED) (USGS NED DEM) (Table 6). The GRASS GIS [25] tool r.in.usgs [51] was used to directly download and import USGS NED DEM at 1/9 arc second (3 m) resolution for the study area (Table 6). The data uses a geographic coordinate system (GCS) with elevation units in meters and the NAD83 horizontal datum and NAVD88 vertical datum. The data was reprojected to the Lambert Conformal Conic projection (NAD83(HARN)/North Carolina, EPSG:3358) and resampled to 3 m resolution using bilinear interpolation. The USGS NED DEM was selected because of its ubiquitous availability and wide use. The UAS DEM was prepared for fusion by resampling the resolution to match the 3 m USGS NED DEM data using bilinear interpolation. The UAS DEM was vertically registered using the overlapping lidar data, imagery, and surveyed points [52] along a stable road feature along the southern edge of Site 1 to calculate a median vertical shift of \(-\)79.97 m. The difference found between the UAS DEM and USGS NED DEM was caused by an ellipsoid/datum difference between the UAS (i.e., Phantom 4 Pro) and USGS NED DEM. The fusion process utilized a 13 pixel (3 m resolution) moving window to define the spatially variable overlap zone with a 30 degree transition angle and 9 m difference reach. The moving window, transition angle, and difference reach were selected based on a visual comparison of multiple fusions. The large transition angle and difference reach improved fusion along borders with large elevation differences along the edge of the fusion. The vertical elevations in the fused DEM were validated against a limited number of available surveyed stormwater inlet elevations, clustered along the southern edge of the site [52].