# Mapping with Pleiades--End-to-End Workflow
### End-to-End 3D Mapping  
This work proposes a complete end-to-end workflow necessary for 3D mapping, where the main goal is to automatically and robustly derive the mapping products from the input data. The input data consists of (1) multi-view satellite images, that can consist of one or multiple stereo pairs, one or multiple triplets or arbitrary combinations like one along track triplet and a single image from another orbit and (2) related metadata, which are the sensor model for each image, reference GCPs and their 2D image coordinates in each given image. Generally, the image coordinates are manually measured within an image-based interactive measurement tool. This information is then employed in an automatic workflow that is comprised of four core tasks sketched schematically in Figure 1. Those tasks are sensor model adjustment, DSM generation, DTM generation and ortho-rectification. The final output of the proposed process are a DSM, a DTM, an nDSM and an ortho-rectified image mosaic, that is, the envisaged 3D mapping products. The flow graph for those tasks is depicted in Figure 2 with the following logic and objectives:  
1. As the initial sensor models are too inaccurate for precise mapping (cf. Reference [21]), those models have to be optimized in the first task. As depicted in the flow graph in Figure 2, the sensor models of each image are optimized separately in the parameter adjustment by employing the initial sensor model together with the GCPs and the image coordinates. The adjusted sensor models are later used in DSM generation and ortho-rectification. Within this task the geo-location accuracy is assessed in detail, that is, assessment of a-priori accuracy as well as assessment of the need and feasibility for optimization, typically by means of GCPs and least squares adjustment. For several test sites and acquisition scenarios, an investigation regarding 2D and 3D accuracy assessment is given. Optimization necessities and potentials are demonstrated in this context. 2. The second task takes all images and their adjusted sensor models as input and derives one final DSM. First, an intermediate DSM is generated from each possible stereo pair. As shown in Figure 2, we propose the following production chain: Epipolar rectification based on the adjusted sensor models followed by stereo matching that yields a disparity map. Spatial point intersection then results in 3D coordinates (i.e., a 3D point cloud) that is resampled into an intermediate DSM. Second, those DSMs are fused and post-processed yielding the final DSM. Within this task, the characteristics of the underlying sensor system is exploited, like availability of image triplets in case of Pleiadesand including high-level processing techniques for the given input. The applicability of high-level processing options, like the semi-global matching (SGM) technique, is investigated, enhanced and resulting achievements are illustrated and validated. 3. In the third task, one DTM and one nDSM are generated solely based on the DSM produced in the previous task. The main steps are ground point filtering, detecting points in the given DSM which are located on bare earth, hole filling to interpolate non-ground information and post-processing mainly for outlier removal. In this way, we can (a) filter man-made structures and vegetation and (b) retrieve vegetation or building heights, finally yielding the DTM and nDSM models. While this is a well-established procedure for airborne light detection and ranging (LiDAR) data, where first and last pulse data can be utilized, much higher complexity is given when terrain needs to be purely extracted from DSMs derived from satellite stereo data. In this respect, algorithmic developments have been made to adapt and improve yet existing approaches to retrieve reliable terrain from such input. 4. The fourth task takes all images, their adjusted sensor models and the final DSM from task two as input and derives one final multi-spectral ortho-rectified image mosaic.