# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.1 Data  
Rapid-DEM utilizes four datasets: satellite imagery from the PlanetScope constellation, crowd-sourced vector data from OpenStreetMap (OSM), existing lidar-based DEM, and UAS-based DEM. PlanetScope and OSM data are used for thematic change detection, while the UAS-based DEM and lidar-based DEM UAS are fused to create an updated DEM (Table 1). PlanetScope is a satellite constellation that provides multispectral daily 3 m resolution satellite imagery consisting of 4 16-bit bands (red, green, blue, near-infrared) [9; 27]. The Surface Reflectance PlanetScope Analytic Ortho Scene image product (Level 3B) was used for analysis because it does not require additional pre-processing or correction before analysis [27]. The imagery has a ground sampling distance (GSD) of 3.7 m and positional accuracy of less than 10 m RMSE [27]. The data is ordered and filtered with the Planet API Python Client [28] to a specific period and an area of interest. To reduce undesired environmental noise during the change detection process, environmental and mechanical filters such as cloud coverage and the off-nadir angle are used. The data is stored in a Google Cloud Storage bucket where GEE accesses it for processing as an ImageCollection [22]. OpenStreetMap (OSM) is a free crowd-sourced map of the world that includes various geospatial features ranging from transportation networks to land use characteristics. OSM attribute data is stored as tags, key-value pairs that are spatially linked to geospatial data structures (node, way, or relation). Nodes are geographic points, ways are lines, closed ways can be interpreted as polygons, and relations define how other elements work together. The data for some of these features is close to complete and highly accurate with worldwide street data being estimated at 80% complete [29]. To automate the development of training data for thematic land cover classification OSM data is accessed through Google BigQuery (GBQ) and converted into a GEE FeatureCollection for the analysis. The OSM data is queried with the tags Natural, Highway, Parking, Building, Surface, and Landuse in addition to specific values types [11; 12; 30]. The OSM FeatureCollection is then mapped to the land cover classification scheme that includes six thematic classes including, buildings, developed, forest, barren, grass, road, and water (Table 2). Each land cover class is generally well represented by the OSM data except for the barren class, which requires additional sources of derived sample data. Consumer-grade UAS data platforms are capable of generating high quality aerial imagery and derived elevation products suitable for geospatial analysis [17; 23; 31]. Sensor data can vary depending on the UAS platform, but most UAS platforms capture the visible spectrum of red, green, and blue bands without customization. The resolution and accuracy of UAS data depend on many factors, including the sensor, flight elevation, image overlap, flight telemetry, time of day, and weather. Ground control points (GCP) are used to validate, as well as to improve horizontal and vertical accuracy when generating 3D terrain models using SfM, but are not always necessary or feasible to utilize [23]. As long as the positional accuracy of the UAS-generated DEM is acceptable for the specific fusion and modeling use case, Rapid-DEM does not require a specific UAS platform. Additionally, there are many existing lidar-based DEMs that can be used to create an updated DEM. However, the choice of which dataset to use depends on the specific application. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Name** & **Data Type** & **Spatial Resolution** & **Temporal Resolution** & **Spatial Extent** & **Data Access** \\ \hline PlanetScope & raster & 3 m & Daily & Global & GEE \\ Lidar-based DEM & raster & Variable & \textgreater{}Annually & Variable & GEE \\ UAS-based DEM & raster & Variable & NA & Local & Local \\ OpenStreetMap & vector & NA & Regularly Updated & Global & GBQ \\ \hline \hline \end{tabular}
\end{table}