# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.3 Prioritizing and Contextualization  
The priority queue algorithm involves seven steps to transform the two thematically classified land cover maps into the priority queue. The before and after land cover maps are transformed into a priority change map (PC) using the priority change table (Table 4). The priority change table maps land cover transition types based on expert knowledge using a seven-point Likert Scale [0; 7]. Zero represents land cover transitions that are not a priority, 1 represents the lowest priority change or noise, and 7 represents the highest priority change. The highest priority change is considered a change in land cover with a high likelihood of modifying the urban topography. For example, cells that change from forested to developed or barren land cover would have a high priority change because they likely indicate new development, which often includes site grading leading to change in topography. The PC map and binary change mask are combined to calculate the mean priority change for each object identified in the binary change mask producing the mean object priority change (MOPC) map. To account for different sized change objects, the product of the MOPC map and the area of each change object (COA) are used to produce the priority (P) map. The priority queue (PQ) map is created by normalizing the values in the P map to real numbers ranging between [0.0, 1.0] using MinMax normalization to improve the PQ map's interpretability. We smoothed irregular boundaries and removed small gaps of each priority object by applying a four-cell buffer computing mode values from surrounding cells. These modifications produce objects that overlap bordering areas (i.e., unchanged) required for data acquisition and fusion. Finally, change objects that do not represent likely topographic change (mean priority value of zero) are removed, and the PQ is converted to vector polygons (VPQ) and sorted in descending order of importance DES(VPQ). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Bands** & **Kernel** & **Source** & **Equation** \\ \hline Original & B, NIR & - & - \\ Median & B, NIR & Square-5 pixels & - \\ Minimum & B, NIR & Square-5 pixels & - \\ Maximum & B, NIR & Square-5 pixels & - \\ NDVI & - & - & [36] & NDVI = \(\frac{\text{NIR}-Red}{\text{NIR}+Red}\) \\ NDWI & - & - & [40] & NDWI = \(\frac{Blue-\text{NIR}}{Blue+\text{NIR}}\) \\ BSI & - & - & [35] & BSI = \(\frac{((Red+Green)-(Red+Blue))}{((\text{NIR}+Green)+(RED+Blue))}\times 100+100\) \\ GLCM & R & Square - 3 pixels & [41] & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification Features. \begin{table} \begin{tabular}{c 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 & 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.