# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.2 Land Cover Classification and Change Detection  
To utilize GEE's computational resources and modeling capabilities, the PlanetScope data ImageCollection is divided at the temporal midpoint and converted into two mosaic images using the median pixel value. These two mosaics represent the before and after images used for classification and change detection. Several change detection methods were explored (e.g., change vectors, multi-date composites), however, the combination of binary and pixel-based post-classification thematic change detection provided the most reliable results. Image differencing is performed on the mosaic images creating a binary change mask by subtracting the red band of the after image from the red band of the before image [32]. A statistical threshold is then set to include pixels less than \(-2.5\) the standard deviation [33; 34]. The threshold and choice of the band are based on visual analysis of the data. The remaining pixels are then clumped into discrete change objects using a plus-shaped kernel with a one-pixel radius, and an adjustable maximum object size (i.e., 128 pixels). The objects are areas that mask the pixel-based change detection and are not used to define segments (i.e., objects or polygons) used in object-based classification. The Bare Soil Index (BSI) [35] and Normalized Difference Vegetation Index (NDVI) [36] are used to differentiate between vegetated and unvegetated surfaces. The BSI is a normalized index that separates bare soil by separating different canopy backgrounds (e.g., bare, sparse, and dense) using the red, green, blue, and near-infrared bands [35]. NDVI uses the red and near-infrared bands to distinguish vegetation health based on the reflectance properties of chlorophyll on visible and near-infrared light [37]. We represented barren land cover using BSI and NDVI thresholds, where \(b_{min}\) and \(b_{max}\) represent the BSI upper and lower threshold and \(n_{min}\) and \(n_{max}\) represent the upper and lower NDVI threshold. \[Barren\ Land=(b_{min}\leq\text{BSI}\leq b_{max})\ AND\ (n_{min}<\text{NDVI}<n_{max}) \tag{1}\]  
To generate barren training samples patches of barren land cover greater than 500 m\({}^{2}\) (e.g., the area of a small parcel) is created by joining connected components with a 1 m kernel. The barren land threshold values are determined through expert interpretation of the data [38] and would require modification to match the spectral characteristics of disparate spatial regions. In some cases, this method may not be applicable to the given dataset and a different method will be needed to supplement barren land cover training samples. The barren land patches and OSM features collection are then combined, and the binary change mask is used to remove features in areas with likely thematic change. The road and water classes are also removed from the training dataset because of their complete and stable representation and are re-included post-classification [11; 29]. The features used for classification are generated separately for the before and after mosaics to account for spatial-temporal non-stationarity of multispectral satellite imagery caused by heterogeneous landscapes and seasonal effects reducing noise during post-classification comparison [20; 39]. The features included summary statistics, low pass filters, spectral indices, and texture (Table 3). The median, standard deviation, minimum, and maximum values are calculated only from the blue and near-infrared bands because \begin{table}
\begin{tabular}{c c c} \hline \hline
**Land Cover** & **OSM Tag** & **OSM Value(s)** \\ \hline Water & Natural & Water \\ Roads & Highway & Residential, Motorway, Trunk, Primary \\ Developed & Parking & Surface \\ Buildings & Building & House, Residential, Retail, Public \\ Barren & Surface & Sand, Dirt \\ Grass & Surface & Grass \\ Grass & Landuse & Grass, Meadow \\ Grass & Natural & Grassland \\ Forest & Natural & Wood \\ \hline \hline \end{tabular}
\end{table}