# Mapping with Pleiades--End-to-End Workflow
## 7 Conclusions  
An end-to-end workflow for mapping with very high-resolution satellite data form the basis for any further semantic analysis. In specific, many applications in remote sensing rely on the following 3D mapping products: (1) DSM, (2) DTM, (3) nDSM and (4) ortho-rectified image mosaic. For this reason, this work described all underlying principles for satellite-based 3D mapping and proposed methods that extract all those products from multi-view stereo satellite imagery. The study was based on, but not limited to, the Pleiades satellite constellation. Besides an in-depth review of related work, the methodological part proposed solutions for each component of the end-to-end workflow. In particular, this included optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, DTM generation and ortho mosaicing. For each step implementation details were proposed and discussed. Another objective of the study was a detailed assessment of the resulting output products. Hence, a variety of test sites were chosen and data sets were gathered representing different acquisition scenarios. The assessment was based on 24 Pleiades images. First, the accuracies of the 2D and 3D geo-location were analyzed. Second, surface and terrain models were evaluated, including a critical look on the underlying error metrics. The differences between single stereo, tri-stereo and multi-view data sets were analyzed as well. Overall, 3D accuracies in the range of 0.2 m to 0.3 m in planimetry and 0.2 m to 0.4 m in height were achieved w.r.t. ground control points. Retrieved DSMs showed normalized median absolute deviations around 0.9 m in comparison to reference LiDAR data. Multi-view stereo outperformed single stereo in terms of accuracy and completeness of the resulting DSMs.  
[PERSON] had the original idea for the study. [PERSON] and [PERSON] supervised the research and contributed to the article's organization. [PERSON] drafted the manuscript, which was revised and approved by all authors.  
No funding has been received for this work.  
The test data used in this study stem from various projects including these Austrian Space Applications Programme (ASAP) projects: HighSens-I (FFG 833435), HighSens-II (FFG 840120) and PleiAlps (FFG 859774). Furthermore, friends and colleagues from JOANNEUM RESEARCH, TU Vienna, Austrian Institute of Technology, GeoVille, National Parks Board (Singapore) and German Aerospace Centre helped by various means. Namely, we want to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} B/H & base-to-height \\ CPU & central processing unit \\ DEM & digital elevation model \\ DSM & digital surface model \\ DTM & digital terrain model \\ GCP & ground control point \\ GPU & graphical processing unit \\ GSD & ground sampling distance \\ ICP & independent check point \\ LiDAR & light detection and ranging \\ MAE & mean absolute error \\ MED & median \\ MRF & Markov random field \\ MSD & multi-directional slope dependent \\ nDSM & normalized digital surface model \\ NMAD & normalized median absolute deviation \\ RMS & root mean square \\ \end{tabular}