# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**From/To** & **Road** & **Building** & **Developed** & **Barren** & **Grass** & **Forest** & **Water** \\ \hline Road & NA & 7 & 4 & 4 & 1 & 1 & 0 \\ Building & 1 & NA & 5 & 7 & 3 & 3 & 0 \\ Developed & 3 & 7 & NA & 7 & 3 & 3 & 0 \\ Barren & 3 & 7 & 5 & NA & 2 & 2 & 0 \\ Grass & 3 & 7 & 5 & 3 & NA & 3 & 0 \\ Forest & 3 & 7 & 7 & 7 & 3 & NA & 0 \\ Water & 0 & 0 & 0 & 0 & 0 & NA \\ \hline \hline \end{tabular} Priority Queue Algorithm:  
1. Create a new Priority Change (PC) map from date 1 and date 2 thematic classifications using priority change table expression
2. Create Mean Object Priority Change (MOPC) map by calculating the weighted mean for each labeled object in the PC map
3. Create Change Object Area (COA) map (a) Create Object Size (OS) map by counting the number of pixels per object (b) Create Object Pixel Area (OPA) map by calculating the area per pixel for the resolution (c) Calculate the Object Area (COA) map as OPA x OS
4. Create Priority (P) map as COA x MOPC
5. Generate Priority Queue (PQ) by normalizing the P map to real numbers between [0.0, 1.0] (Equation (2)).
6. Export PQ to a vector polygon Vector Priority Queue (VPQ)
7. Sort queue in descending order DESC(VPQ)  
\[\text{PQ}=\frac{\text{P}-min(\text{P})}{max(\text{P})-min(\text{P})} \tag{2}\]  
The priority queue is contextualized through a priority context map (PCM) that explains the type of land change. The PCM is created by assigning each land cover change transition and action. These actions are defined by two base types of land change: New features and removed features (Table A1). New features are represented by changes such as the construction of a new building, road, parking lot, while removed features represent change such as forest clearings or denolished buildings. Static land cover and land change likely due to noise (e.g., seasonal variation, misclassification) are not included. To assign a final context to each priority change object the mode land cover change context class is calculated for each object in the priority queue.  
#### 2.1.4 UAS Data Acquisition and Processing  
A flight plan of a selected high priority area is generated with a priority queue defined polygon or a manually defined area determined by the UAS pilot using a flight planning and control application such as Drone Deploy [44]. However, other UAS flight planning and mapping software solutions would work to capture the updates. Additionally, to ensure safety and compliance with local UAS regulations, a qualified UAS pilot should review all flight plans generated using priority queue-defined polygons. The use of GCPs is recommended to improve horizontal and vertical accuracy of the flight data, but is not necessary to produce a DEM with acceptable vertical accuracy for fusion in all locations [45; 46].  
The collected UAS data is processed using the open-source photogrammetry software WebODM [24], which provides a web user interface to OpenDroneMap's (ODM) [47] implementation of SfM. SfM processes images with embedded positional information from the UAS GNSS receiver and outputs an orthomosaic and DEM(s), e.g., bare ground digital terrain model (DTM) and a digital surface model (DSM) with above-ground features.  
\end{table} 
Table 4: Priority Change Table. The UAS-based DEM can also be generated from other photogrammetric software solutions that generate comparable accuracy in output DEM [48].