## 2 Related Work
[16] compared the calculated PlanetScope DEMs to models generated by stereo photogrammetry usingcommercial stereoscopic VHR satellite images, i.e., GeoEye-1 and WorldView-2, as well as to the SRTM DEM in stable areas. The NMADs are about 7 m and 12 m compared to the VHR DEM and the SRTM DEM, respectively. The larger differences to the SRTM DEM may result from its lower resolution and overall accuracy [25]. No comparison to independent reference data was conducted by [23], but a relative error of 10 m, calculated by comparing the PlanetScope DEMs with each other in stable terrain, was reported. Mass movements of the surging glacier could be clearly identified in both studies. Ref. [20] reported a root mean square error (RMSE) of 5.5 m comparing the PlanetScope DEM to LiDAR data. Similarly, the authors of Ref. [19] reported an RMSE of about 5 m, also comparing the derived PlanetScope DEM to LiDAR data. They found that volumetric changes can be detected when the signal exceeds 8-10 m in a vertical direction. All the studies emphasize that the DEM generation using PSS images strongly depends on the data availability regarding suitable base-to-height ratios, which in most cases is very small due to the image configurations. However, this can be compensated to some extent due to the (often) abundance of possible image pairs with similar footprints from slightly different perspectives and thus good matching and beam intersection properties. The time span in which possible image pairs are taken should always be chosen in relation to the dynamics of the ROI to reduce errors induced by changing objects. The studies also found that the supplied RFMs describe the imaging geometry very well, and only minor biases in the range of 0.05-0.2 px are to be expected, which could be solved with the help of bundle-block adjustment, e.g., [23; 26]. In a nutshell, DEMs from multi-view PlanetScope imagery show height accuracy potentials of about 1-2 px, i.e., 4-8 m. They can provide valuable information for studying volumetric changes at the global scale and are well suited to investigate Earth dynamics like landslides and glacier changes in four dimensions. All the workflows described in the literature are similar but require either non-published in-house implementations or commercial software solutions for the processing of the data. In this paper, we present an easy-to-use open-source stereo processing pipeline for generating DEMs from multi-temporal PlanetScope images, which requires only a few steering parameters and is aimed at a target group from applied geosciences and glaciology in particular. A very promising open-source toolbox for photogrammetric 3D reconstruction using space-borne data is ASP. ASP includes optimized algorithms for image block pre-processing (e.g. bundle-block adjustment), stereo matching (e.g. SGM), and post-processing (e.g. error assessment, outlier removal, point cloud registration, and DEM rasterization). All the tools can be loosely combined and parameterized for a wide variety of input data and application purposes. Details on ASP are given in [27; 28]. ASP has already been successfully deployed for processing data of SkySat imagery, a satellite constellation also launched by Planet Labs in 2013. Moreover, [29] published the open-source pipeline _SkysatStero_ using tools from ASP for DEM generation from SkySat-C image triplets and video sequences (GSD of the panchromatic images approximately 0.7 m). The pipeline was successfully applied to generate DEMs of the Mount Rainier glaciers, with GSDs of 2 m showing vertical accuracies of 2-3 m. Recently, the authors of Ref. [12] presented an approach in which ASP was used to generate a DEM from PlanetScope data. They referred to a specific use case and used only two images with a rarely achievable configuration and large convergence angle to generate a relatively small and coarse-resolution DEM with a GSD of 30 m and demonstrated the suitability of PlanetScope data and ASP for stereo reconstruction.