Global Navigation Satellite Systems (GNSS) have revolutionized land surveying, by determining position coordinates with centimeter-level accuracy in real-time or up to sub-millimeter accuracy in post-processing solutions. Although low-cost single-frequency receivers do not meet the accuracy requirements of many surveying applications, multi-frequency hardware is expected to overcome the major issues. Therefore, this paper is aimed at investigating the performance of a u-blox ZED-F9P receiver, connected to a u-blox ANN-MB-00-00 antenna, during multiple field experiments. Satisfactory signal acquisition was noticed but it resulted as >7 dB Hz weaker than with a geodetic-grade receiver, especially for low-elevation mask signals. In the static mode, the ambiguity fixing rate reaches 80%, and a horizontal accuracy of few centimeters was achieved during an hour-long session. Similar accuracy was achieved with the Precise Point Positioning (PPP) if a session is extended to at least 2.5 h. Real-Time Kinematic (RTK) and Network RTK measurements achieved a horizontal accuracy better than 5 cm and a sub-decimeter vertical accuracy. If a base station constituted by a low-cost receiver is used, the horizontal accuracy degrades by a factor of two and such a setup may lead to an inaccurate height determination under dynamic surveying conditions, e.g., rotating antenna of the mobile receiver.

In this study, Differential Interferometric Synthetic Aperture Radar Interferometry (DInSAR) of artificial Corner Reflectors (CRs) were validated in the area of fast and nonlinear deformation gradient caused by active coal longwall exploitation. Three Sentinel-1 datasets were processed using conventional DInSAR, Persistent Scatterer Interferometry (PSI), and Small BAseline Subset methods implemented in ENVI SARscape™. For evaluation, leveling and Global Navigation Satellite System (GNSS) measurements were used. Considering the challenge of snow cover, the removal of all winter images was not a successful strategy due to the long temporal baseline and strong movement, which cause phase unwrapping problems and underestimate the real deformation. The results indicate that only conventional DInSAR and SBAS with low network redundancy allow us to capture maximal deformation gradient and the root mean square error calculated between the CRs and the ground truth is on the level of 2–3 cm for the vertical and easting deformation component, respectively. For the small deformation gradient represented by the permanent GNSS station (4 cm/year), all SBAS techniques appeared to be more accurate than DInSAR, which corresponds to higher redundancy and better removal of the atmospheric signal. In contrast, DInSAR results allowed to capture information about two subsidence basins, which was not possible with SBAS and PSI approaches.

The Upper Silesian coal basin (USCB) in Poland faces significant ground deformation issues resulting from mining activities conducted without backfill, which can persist for years. These activities can cause damage to surface structures and phenomena such as induced seismicity. Ground deformations can be monitored using differential synthetic aperture radar interferometry (DInSAR). However, various DInSAR approaches have their own advantages and limitations, particularly regarding accuracy and atmospheric filtering. This is especially important for high-frequency displacement signals associated with seismic activity, which can be filtered out. Therefore, this study aims to assess the detectability of mining-induced seismic events using interferometric techniques, focusing on the USCB area. In this experiment, we tested two InSAR approaches: conventional DInSAR without atmospheric filtering and the small baseline subset (SBAS) approach, where the atmospheric phase screen was estimated and removed using high-pass and low-pass filtering. The results indicate that, in most cases, post-seismic ground displacement is not detectable using both methods. This suggests that mining-related seismic events typically do not cause significant post-seismic ground displacement. Out of the 17 selected seismic events, only two were clearly visible in the DInSAR estimated deformation, while for four other events, some displacement signals could neither be definitively confirmed nor negated. Conversely, only one seismic event was clearly detectable in the SBAS displacement time series, with no evidence of induced tremors found for the other events. DInSAR proved to be more effective in capturing displacement signals compared to SBAS. This could be attributed to the small magnitude of the tremors and, consequently, the small size of the seismic sources. Throughout the investigated period, all registered events had magnitudes less than 4.0. This highlights the challenge of identifying any significant influence of low-magnitude tremors on ground deformation, necessitating further investigations. Moreover, SBAS techniques tend to underestimate mining displacement rates, leading to smoothed deformation estimates, which may render post-seismic effects invisible for events with low magnitudes. However, after an in-depth analysis of the 17 seismic events in the USCB, DInSAR was found to be more effective in capturing displacement signals compared to SBAS. This indicates the need for significant caution when applying atmospheric filtering to high-frequency displacement signals.

The second Polish realisation of the European Plate Observing System (EPOS), namely EPOS-PL+ project (2020-2023), comprised a dedicated task for development of an Infrastructure Centre for Satellite Data Research (CIBDS in Polish). The main task of the centre was to create a methodology for monitoring, modelling and prediction of the terrain deformations related with the extensive underground mining works taking place in the region of the Upper Silesian Coal Basin (USCB). This area is characterised with extremely dynamic surface changes consisting of small-scale deformation bowls (200-300m in radius) within short range from each other. The subsidence could reach between 0.6 up to 1.6 m per year, depending on the depth of the coal seams under explorations. The deposits are in depth between 400 and 1200 m, and are exploited in a multi-layer manner. The dynamics of the appearance of the subsidence patterns over time is closely related to the long-wall mining method used in this mining area. Within the CIBDS several modules for processing of Synthetic Aperture Radar (SAR) data have been developed. An automatic system for Differential Interferometric SAR (DInSAR) processing and postprocessing was developed based on the Alaska data facility repository of Sentinel-1 data, and the European Space Agency (ESA) tools SNAP and snappy. A new methodology was introduced for integration of lower quality but more detailed DInSAR terrain deformation maps with products created by the usage of Persistent scatterers (PSInSAR) technique, which have higher accuracy but lower coverage. The integrated deformation maps are validated with the results of campaign in-situ GNSS/levelling measurements.A new methodology for modelling of the subsidence and 1-month prediction of the expected deformations have been designed on the basis of the Knothe-Budryk theory. FOr the purpose, artificial intelligence (AI) capabilities have been applied using the deformation maps generated by the DInSAR processing and external information about the rhythm and range of the mining works.The newly developed system for terrain changes monitoring target the gaps that left in the commonly used platforms like European Ground Motion Service (EGMS) that cannot cover very extensive deformations and to support and upgrade the mining management and supervision.

This study focuses on classifying height differences in the area of the Marcel mine in the Upper Silesian Coal Basin, using UAV photogrammetry, UAV laser scanning, and InSAR coherence data. The random forest (RF) algorithm was selected for this purpose. The RF classifier used features calculated based on the digital terrain model of differences, InSAR coherence data, digital terrain, and surface models with their derivatives. Besides the 6-day coherence to capture temporal coherence variability, key statistical parameters of the time series were calculated. The model achieved an accuracy of 79 % and an F1-score of 81 %, effectively distinguishing mining-related displacements from other classes. The results indicate that incorporating InSAR coherence improved the separation of classes, but certain limitations, such as data resolution and the temporal similarity of features, suggest the need for further research.