## 5 Conclusions  
In this article, we presented an approach for efficiently generating DEMs of landscapes using an easy-to-use open-source pipeline called _Planet4 Stereo_. This method enables the processing of multi-temporal, inherently non-stereo, and cost-effective PlanetScope imagery. Our experiments demonstrate that the results produced by the pipeline are comparable in quality to those described in the literature, which often rely on proprietary or less-accessible tools. The pipeline empowers researchers in fields such as geomorphometry and glaciology to derive valuable insights into landscape deformation with high spatiotemporal resolution without requiring extensive expertise in photogrammetry. The core of the pipeline, ASP, is continuously evolving and integrating new methods that could further enhance stereo matching capabilities. In _Planet4 Stereo_, only minor adjustments to the script (e.g., changing the matching method flag) are necessary in this case. At the same time, Planet Labs is developing new generations of satellites. Future work could focus on both the technical advancement of _Planet4 Stereo_, incorporating updates from ASP, and evaluating its suitability for data from upcoming satellite generations. Furthermore, key components of the pipeline--particularly the stereo matching and triangulation stages--could be substituted with corresponding modules from other open-source tools, such as MicMac [43], to assess potential improvements in disparity estimation accuracy and surface reconstruction efficiency. Several limitations warrant discussion. The current approach relies on approximation data for co-registering the pairwise-generated PSS DEMs, which may introduce biases in areas where the approximation DEM is less accurate or outdated. This issue is particularly pronounced in dynamic environments, such as fast-moving glaciers, where rapid surface changes challenge co-registration accuracy. Integrating multi-source datasets, such as Sentinel-1 synthetic-aperture radar (SAR) data (GSD of 20 m, six-day revisit), may enhance robustness by providing approximation data with minimal temporal offset to the used PlanetScope images. Additionally, SAR data could help to fill in gaps in PlanetScope-derived DEMs, such as in shadowed or cloud-covered areas, although resolution differences necessitate extrapolation. While SAR-derived DEMs have lower spatial resolution, their resilience in adverse conditions and ability to capture dynamic changes could improve co-registration and overall DEM quality. However, effective interferometric SAR (inSAR) processing requires a comprehensive understanding of, e.g., baseline geometry and phase decorrelation [44]. [PERSON] conceptualized the study, developed the data analysis, and led the manuscript writing. [PERSON] provided expertise in methodology and statistical validation and assisted in the refinement of the results. The research proposal that secured the primary funding for this study was developed and submitted by [PERSON]-[PERSON], who also contributed to the study design and scientific supervision. All authors reviewed and approved the final manuscript. We would like to thank Planet Labs for providing the image data free of charge as part of their Education and Research Program. This work was funded by the German Research Foundation (DFG) project Glacier4D (grant number 436500674). Data Availability Statement: Planet4 Stereo is available on GitHub: [[https://github.com/mel-ias/planet4](https://github.com/mel-ias/planet4) stereo]([https://github.com/mel-ias/planet4](https://github.com/mel-ias/planet4) stereo) (accessed on 16 April 2025). The release version 1.0.0 is also available in [45].