This paper reports on the activities carried out in the context of “Dragon project 32278: Three‐ and Four‐Dimensional Topographic Measurement and Validation”. The research work was split into three subprojects and encompassed several activities to deliver accurate characterization of targets on land surfaces and deepen the current knowledge on the exploitation of Synthetic Aperture Radar (SAR) data. The goal of Subproject 1 was to validate topographic mapping accuracy of various ESA, TPM, and Chinese satellite system on test sites in the EU and China; define and im-prove validation methodologies for topographic mapping; and develop and setup test sites for the validation of different surface motion estimation techniques. Subproject 2 focused on the specific case of spatially and temporally decorrelating targets by using multi‐baseline interferometric (In‐ SAR) and tomographic (TomoSAR) SAR processing. Research on InSAR led to the development of robust retrieval techniques to estimate target displacement over time. Research on TomoSAR was focused on testing or defining new processing methods for high‐resolution 3D imaging of the inte-rior of forests and glaciers and the characterization of their temporal behavior. Subproject 3 was focused on near‐real‐time motion estimation, considering efficient algorithms for the digestion of new acquisitions and for changes in problem parameterization.

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.