## 4 Results and Discussion
### Morenci Mine (USA)-SkySat  
The Morenci mine is known for its high copper production. For example, in 2016, 848 million pounds were produced [31]. The test site is shown in Figure 6. The ellipsoidal terrain heights of the region of interest ranges from 1048 m to 2417 m and spans over an area of ~122 km\({}^{2}\), consisting of mountainous and vegetated areas, rugged terrain and buildings. A triplet of stereo SkySat images was acquired on 28 January 2019 (Table 1). We used both the basic-panchromatic and basic-multispectral L1 Bs to generate a DEM and compared it with a high-quality of LiDAR DEM available from the 3D Elevation Program (3 DEP). This program is managed by the U.S. Geological Survey (USGS) National Geospatial Program to respond to the growing need for high-quality topographic data [32]. The LiDAR DEM is available as raster data in the UTM 12 North map projection and WGS84 datum at 1 m GSD with ellipsoidal heights. The LIDAR data were acquired 4 years before SkySat acquisitions, a period during which changes have occurred due to mining activity and vegetation growth or clear-cut. The LiDAR DEM was downloaded via Global Mapper software. Figure 5: Schematic workflow for PlanetScope image geometry refinement and 3D extraction. We refined the RFMs of image triplets of each sensor separately as described in the method section. Each of the 174 scenes is separated into three subsets corresponding to the three tri-stereo sensors. Then we estimated a 3D transformation using the overlap between the different detectors, in order to adjust the sensor-1 and -3 group to the sensor-2 group. To that effect, about 149,419 tie points were extracted and matched using the SIFT+ feature detector included in the open-source software MicMac [33, 34]. This particular algorithm allows the identification of points efficiently in large images, irrespective of their position and is robust to affine transformations (changes in scale, rotation, size and position) and contrast variations [35]. After correcting the RFMs using a first-order polynomial obtained from our least-squares procedure, the overall re-projection error, estimated from the standard deviations of the distribution of residuals, is 0.42 pix. Given the nominal GSD of 71 cm, it corresponds to a geo-location error of \(\sim\)37 cm on the ground. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Acq. Date and** & **Image ID** & **Nb. of Scenes** & **View Angle (\({}^{\circ}\))** & **Sun Elev. (\({}^{\circ}\))** & **GSD (m)** \\ \hline
28 January 2019 & s106\_20190128T204710Z & 51 (17/detector) & 26.2–28 & 35.6 & 0.8–0.9 \\  & s106\_20190128T204819Z & 54 (18/detector) & 25.9–27.8 & 35.6 & 0.8–0.9 \\  & s106\_20190128T204744Z & 69 (23/detector) & 8–8.3 & 35.5 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acquisition parameters for the SkySat tri-stereo over Morenci Mine study area. Figure 6: The test site Morenci Mine, AZ, USA. (**a**) Subset of Sentinel-2 true color ortho-image (TCI) over the study area captured on 1 February 2020; (**b**) Outlines of SkySat triplets on a Sentinel-2 ortho-image. [PERSON], [PERSON] and Blue bounding boxes delineate the individual scenes of along track forward, nadir and backward views, respectively; (**c**) Tri-stereo RGB SkySat scenes for visual interpretation, from left to right, backward, nadir and forward. We generated DEMs in the image and object spaces. The panchromatic scenes, and our refined RFMs, were imported to Metashape software [36; 37] to perform the DEM reconstruction in object space. First, a multi-view stereo matching on the aligned scenes was applied using a variant of Semi-Global Matching algorithm in object space (OSGM) [29]. A total of 4,785,824 points were generated over an area of 123 km\({}^{2}\). Second, a 3D mesh model was created from the dense point cloud. Then, a mesh-based DSM was extracted. The GSD of the mesh-based DSM was set to 3 m. Figure 7 shows the complete \(\sim\)123 km\({}^{2}\) DSM. Elevations vary within 1.2 km and to 2.2 km.