Underground mining, regardless of the excavation method used, has an impact on the terrain surface. For this reason, continuous monitoring of the ground surface above the excavations is necessary. Deformations on the ground surface occur with a time delay in relation to the mining works, which poses a risk of significant deformations in built-up areas, leading to building disasters. In addition to monitoring, it is therefore necessary to forecast displacements, which at present is usually done using the empirical integral models, which describes the shape of a fully formed subsidence basin and require detailed knowledge of the geological situation and parameters of the deposit. However, insufficiently precise determination of coefficients may lead to significant errors in calculations. Machine learning can be an interesting alternative to predict ground displacement in mining areas. Machine learning algorithms fit a model to a set of input data so that it best represents all the correlations and trends detected in the set. However, the fitting process must be controlled to avoid overfitting. The validated model can then be used to detect new deformations on the ground surface, categorize the resulting displacements, or predict the value of subsidence. In this case ARIMA model (Auto-Regressive Integrated Moving Average) was used to predict deformation values for single points placed in the centers of the subsidence basins in the LGCB (Legnica-Głogów Copper Belt) area. The InSAR time series calculated using the SBAS method for the years 2016-2021 was used as input data. The results were compared with the persistence model, against which there was an improvement in accuracy of several percentage points.

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking.

The article assesses the condition of cartographic research in the context of young scientists. Based on an analysis of the 469 academic achievements of 77 young Polish cartographers in the 2016–2022 period, the authors highlight challenges in defining contemporary cartography and difficulties in classifying research due to its interdisciplinary nature, overlap with GIS, and the use of cartographic methods in studies from other fields. The study also examines thematic trends, mobility constraints, and patterns of scientific collaboration. The findings emphasise the need to support young researchers and foster interinstitutional cooperation.

The paper presents the potential of combining satellite radar data and neural networks for quasi-automatic detection of glacier grounding lines. The conducted research covered five years and was carried out in the area of the Amery Ice Shelf. It has a very complex shoreline, so its grounding-line location is uncertain. Thus, it has always been the subject of much research. The main objective of our work was to find out if Synthetic Aperture Radar data combined with a deep learning implementation would enable rapid detection of ice shelf grounding lines over large areas. For this purpose, 290 radar images from the Sentinel-1 satellite covering 46 000 km2 were used. Processed by the Differential Interferometry of Synthetic Aperture Radar four-pass method, the images formed a time-consistent series between 2017 and 2021. As a result of performed calculations, a total length of 1280 km of grounding line was determined. They were validated by comparing with other independent data sources based on manual measurements. It has been demonstrated that the combination of satellite radar data and automated data processing allows for obtaining high-precision results continuously in a very short time. Such an approach allows monitoring of grounding line position in the long term with intervals of less than one week. It enables analysis of the dynamics changes with unprecedented frequency and the identification of patterns.

Synthetic aperture radar interferometry (InSAR) is an effective tool for large area measurements and analysis, including topography measurements or ground surface subsidence caused by mining operations, earthquakes, or volcanic activity. However, the accuracy of these measurements is often limited by the disturbances that arise during the microwave propagation process in the ionosphere and troposphere. The atmospheric delay in the interferometric phase may cause the detection of terrain surface changes to be impossible or significantly distorted.  In our proposed approach, we propose a complete workflow to computing a time series from raw data obtained with the Sentinel-1 mission. The solution consists of a Small Baseline Subset (SBAS) algorithm with an implementation of the split range-spectrum method and the Generic Atmospheric Correction Online Service (GACOS) model. The proposed solution was used in time series calculations of SAR data in two areas: northern Chile and Taiwan. It is demonstrated that simultaneous allowance for both the tropospheric and ionospheric corrections significantly improves final results.

Measuring how the Earth’s surface deforms in response to natural and anthropogenic processes allows scientists to better understand them and the risks associated with them. Synthetic aperture radar interferometry (InSAR) enables monitoring the land surface in order to detect and trace changes of altitude which result from such phenomena as volcanic, seismic, landslides, or glacier movements (natural) and mining and post-mining, water pumping, or gas storage (anthropogenic). Simultaneously trace changes of land surfaces having an area up to approx. 48,000 km (in the case of Sentinel 1A/1B satellites for one scene) with an interval of between 6 and 12 days (due to the time required for SAR data acquisition) allows to monitor surface displacements in regional or national scale. We present results of measurements covering the entire Poland for the period 6  years, from 2015 to the end of 2020. The time series were developed based on the SBAS algorithm. Due to shifts of scenes in the azimuth direction in a given path, we developed an algorithm to crop and merge the raw imagery to predefined ranges. This allowed to formulate a series of interferometers with the same ranges, uniformly covering the entire area. Finally, we performed calculations of 32 frames (14 from ascending and 18 descending orbits). Based on time series, identified numerous areas where significant, anthropogenic or natural nature, surface displacement appears. The collected displacement observations allow not only to monitor areas where displacement occurs continuously, e.g. as a result of direct effects of mining operations, but also to identify regions of new, previously unidentified displacement, displacements occurring temporarily, or very slow movements of large areas.