# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
For example, the United States Geological Survey (USGS) 3D Elevation Program (3 DEP), an improvement on the National Elevation Dataset (NED), serves to combine and standardize localized airborne lidar from various data sources into a seamless national dataset [16]. The 3 DEP program has specific standards and requirements of source data (e.g., spatial reference system, horizontal resolution, vertical resolution, etc.) designed to improve the overall quality of the derived multi-source products [16]. A recent study by [PERSON] and [PERSON] evaluated assumptions that these data requirements provide enough standardization to combine these disparate elevation datasets into a single dataset and whether the fusion of high-resolution lidar data improves the accuracy of derived products [16]. Their results demonstrated that even with systemic requirements for data inclusion, errors still exist in the accepted datasets [16]. However, they found that the vertical accuracy outperforms global DEM datasets and that the fusion of lidar data increases the overall vertical accuracy of the 3 DEP seamless data products [16]. These results are consistent with other studies that examine fusing high-resolution UAS-based DEM with lidar-based DEM to improve source data accuracy [17; 18]. Recent research in land cover classification has shown that the fusion of multi-source remote sensing data with elevation data may increase the accuracy of derived land cover classification products. These studies align with [PERSON] and [PERSON]'s results, which identified statistically significant differences between the mean elevation range and height above ground elevation between distinct land cover classes [16]. Digital elevation models have lower temporal resolutions than other types of geospatial data, such as high-resolution imagery and urban infrastructure data, because topographic change usually occurs slowly over large spatial extents. Additionally, traditional methods to update DEMs (e.g., airborne lidar surveys) are costly and therefore performed infrequently. However, in areas where rapid urbanization is modifying topography, DEMs no longer accurately reflect the landscape, increasing uncertainty in the geospatial models that depend on them (e.g., stormwater, flooding) [4; 19]. These topographic changes are generally limited and spatially dispersed, covering only a small portion of the total area surveyed by airborne lidar or long-range unmanned aerial systems (UAS) (i.e., capable of flying entire cities). Satellite-based land cover change detection [20; 21] and DEM data fusion with low-cost UAS provide a toolset to update these small area changes [17; 18] by providing methods to identify where topographic changes occurred and a method to update an existing lidar-based DEM seamlessly [17; 18]. However, the use of these methods alone is limited by the potentially large number of affected areas combined with the flight time limitations of low-cost UAS. Therefore, a method is needed to prioritize where to send UAS to capture the most significant topographic changes for fusion and update of existing DEM. With the emergence of new earth observation satellite constellations (PlanetScope [9]), cloud-based computational platforms (Google Earth Engine [22]), and open-source UAS flight planning [23], processing, and data fusion methods [18] (WebODM [24] and GRASS GIS [25]) new opportunities are available to develop novel methods in DEM data acquisition and fusion. We developed Rapid-DEM, a framework that prioritizes areas of topographic change for UAS survey and data fusion using multispectral satellite data to detect, classify, and prioritize land cover change transitions with a high likelihood of topographic change in a priority queue. The priority queue utilizes the relationship between land cover data and DEM data as a proxy for the likelihood and magnitude of topographic change.