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One of the critical factors influencing the accuracy of a local geoid model is the quality of the digital elevation model (DEM). A properly selected high-resolution DEM can significantly mitigate errors in geoid modeling, gravity anomaly processing, and topography and downward continuation correction. Purpose. Evaluating the accuracy of five global DEMs obtained from open sources to identify the most suitable model for creating a local geoid. Methodology. The vertical accuracy of the DEMs was assessed by comparing the heights between the DEM and control points across different types of terrain. The reference values are based on 344 ground benchmarks, where GNSS observations were performed with subsequent adjustment of coordinates and heights. The accuracy analysis involved calculating statistical indicators of the height differences between the GNSS data and the DEM data. Findings. The standard deviation assessment showed favorable values for the COPERNICUS and ALOS DEMs, followed by SRTM, ASTER, and ETOPO. In the mean absolute error calculations for mountainous areas, the ALOS model performed best, followed by COPERNICUS, SRTM, ASTER, and ETOPO. For other types of terrain, COPERNICUS demonstrated the best results in mean absolute error. Originality. This study distinguishes itself through the incorporation of advanced high-resolution DEMs, such as GLO30, providing a modern and thorough evaluation of DEM accuracy specifically for Kazakhstan. What is new is a detailed analysis of the impact of terrain features (plain, hilly, mountainous) on modeling accuracy. This approach advances beyond previous assessments, delivering new and significant insights into the performance of contemporary DEMs. Practice value. The practical value of the results obtained consists in issuing recommendations regarding the possibility of using the studied DEM for the regions of Kazakhstan which differ among themselves in terms of landscape characteristics. The findings indicate that COPERNICUS and ALOS DEMs are highly suitable for precise geoid modeling in southern Kazakhstan. These models can significantly improve the accuracy of local geoid models, benefiting applications in geospatial science and engineering. [ABSTRACT FROM AUTHOR]

Purpose. Reducing the dispersion of radar reflections from local objects, with multi-frequency sensing, to solve the problem of orientation by radar reflections from objects. Methodology. Reflections from local objects in the entire frequency range of the radar station (RS) at the same radio engineering position were measured by three independent radars of the same type on different days and at different antenna elevations. The deviation of the radar stations in the position did not exceed 500 meters. Coordinates (azimuth, range) of reflections of several separate local objects were allocated for each radar. The average values of reflections from local objects and their dispersion in the frequency range were calculated. Using various algorithms, individual frequencies were sampled and the reflected signals were averaged at these frequencies. The decrease in the dispersion of the reflected signal from the number of frequencies at which reflections were measured and from the algorithm for selecting these frequencies was investigated. Findings. Averaging the values of reflections from local objects for several frequencies leads to a decrease in dispersion and, as a result, to a more accurate correspondence of the reflected signal level to the geometric size of the local object. The variance decreases most rapidly for a small number of frequencies selected for averaging when selecting frequencies located in an interval of at least 1% relative to each other. Originality. To solve the problem of orientation based on radar reflections from local objects, it is necessary to identify the landmarks selected on a digital terrain model. Due to the fact that local objects (hills) are a collection of many reflectors falling into the allowed volume of the radar, with different levels of reflections and random phases, there may not be radar reflection from a local object at a certain frequency, or it may be very small. In order to unambiguously identify all landmarks, measurements must be carried out at several frequencies. The work has established how many frequencies measurements should be performed at and on what principle these frequencies should be selected. Practical value. The advent of digital terrain models made it possible to solve the problem of terrain orientation by comparing radar reflections from local objects with reflection models based on digital terrain maps. Radar reflection models use mathematical expectations of reflection values, unlike real reflections, which have random deviations in signal levels depending on the operating frequency. Reducing the variance of these deviations increases the accuracy of identifying characteristic local objects (landmarks) used to orient the radar in the absence of data from satellite navigation systems. [ABSTRACT FROM AUTHOR]

Classifying and filtering non-ground objects from the point cloud data are among the major challenges to the development of a digital terrain model (DTM). This paper proposes a hierarchical deep network to filter and classify non-ground objects from the point cloud data. The proposed network is mainly based on a deep encoder-decoder network with effective convolutional connections for extracting and fusing the features of shallow and deep layers to detect the objects better. In the proposed encoder-decoder network, a feature extraction block was designed to extract various features at different levels. The network also adopts a global-local feature fusion strategy. The proposed hierarchical deep network is based on the extraction and gradual fusion of features on different scales to extract objects of various dimensions and densities in urban areas with different topographical conditions. Evaluation of the proposed deep network in US and ISPRS datasets indicated its high accuracy of object detection in complicated and dense areas. In the US dataset, our model reduced total error by 0.055 and increased kappa by 20.18% on average compared to the second-best method. According to the results of a performance analysis through the ISPRS data, the proposed deep network outperformed the other methods and reduced the total error by 0.130 compared to the existing methods. [ABSTRACT FROM AUTHOR]

Digital terrain models (DTMs) are created using elevation data collected in geological surveys using varied sampling techniques like airborne lidar and depth soundings. This often leads to large data sets with different distribution patterns, which may require smooth data approximations in irregular domains with complex boundaries. The thin plate spline (TPS) interpolates scattered data and produces visually pleasing surfaces, but it is too computationally expensive for large data sizes. The finite element thin plate spline (TPSFEM) possesses smoothing properties similar to those of the TPS and interpolates large data sets efficiently. This article investigated the performance of the TPSFEM and adaptive mesh refinement in irregular domains. Boundary conditions are critical for the accuracy of the solution in domains with arbitrary-shaped boundaries and were approximated using the TPS with a subset of sampled points. Numerical experiments were conducted on aerial, terrestrial, and bathymetric surveys. It was shown that the TPSFEM works well in square and irregular domains for modeling terrain surfaces and adaptive refinement significantly improves its efficiency. A comparison of the TPSFEM, TPS, and compactly supported radial basis functions indicates its competitiveness in terms of accuracy and cost.

Prompt receipt of reliable information about the terrain with sufficient detail is one of the main tasks in the fields of national economy, territorial development or research of large territorial units. The multiplicity of error sources in Earth remote sensing materials is due to a number of factors, and the resulting terrain models have a certain degree of generalization, which directly affects the correctness of digital terrain models. This article is devoted to the analysis of existing methods for estimating errors of open digital terrain models in order to increase their accuracy. Correct digital elevation models have a high similarity to reality and can be used in regional studies to determine the morphometric indicators of the territory.

Modeling based on a digital relief model of the spatiotemporal change in the amount of total solar radiation and the assessment of the contribution of locations to the distribution of meteorological elements.DEM analysis was conducted in a GIS environment to assess the spatial and temporal distribution of total solar radiation, and statistical analysis of retrospective climatic and actual (2023) meteorological data was conducted.Based on a digital model of the relief of average spatial resolution in a GIS environment, changes in the spatial and temporal magnitude of solar radiation were estimated. A prospective analysis of climatic conditions made it possible to assess the general features of the climate of the Makazhoy basin Field observations made it possible to characterize modern climatic conditions, as well as to calculate gradients.Depending on the locations, it was determined that the amount of total solar radiation in the Makazhoy basin varies by almost 3 times. Within the same altitude intervals, the difference between the slopes of the southern and northern exposures reaches up to 50 %. The smallest differences in the amount of radiation were observed on the days of the summer solstice, and the largest on the days of the winter solstice. The average annual air temperature in the altitude range of 1960–2264 m in 2023 varied from 8.6 to 5.8°, and the amount of precipitation – from 481 to 525 mm, respectively. The intra‐annual course of meteorological parameters (air temperature and leaf surface, precipitation and relative humidity) within the meteorological transect has been revealed. The data obtained make it possible to proceed to the formation of a GIS system for monitoring the climatic conditions of the carbon landfill of the Kadyrov Chechen State University.

The combination of Remote Sensing and Deep Learning (DL) has brought about a revolution in converting digital surface models (DSMs) to digital terrain models (DTMs). DTMs are used in various fields, including environmental management, where they provide crucial topographical data to accurately model water flow and identify flood-prone areas. However, current DL-based methods require intensive data processing, limiting their efficiency and real-time use. To address these challenges, we have developed an innovative method that incorporates a physically informed autoencoder, embedding physical constraints to refine the extraction process. Our approach utilizes a normalized DSM (nDSM), which is updated by the autoencoder to enable DTM generation by defining the DTM as the difference between the DSM input and the updated nDSM. This approach reduces sensitivity to topographical variations, improving the model's generalizability. Furthermore, our framework innovates by using subtractive skip connections instead of traditional concatenative ones, improving the network's flexibility to adapt to terrain variations and significantly enhancing performance across diverse environments. Our novel approach demonstrates superior performance and adaptability compared to other versions of autoencoders across ten diverse datasets, including urban areas, mountainous regions, predominantly vegetation-covered landscapes, and a combination of these environments. [ABSTRACT FROM AUTHOR]

A cartographic assessment of the dynamics of residential and agricultural natural resource use in the territory of the Selenga River delta based on topographic maps for different points in time for 1952‒2015 is presented. This period is characterized by the regulation of the level of Lake Baikal to maintain the operation of the Irkutsk hydroelectric power station and by a transformation of the system of natural resource use at the Selenga River delta and the lake shore. Based on vectorization of retrospective layers of topographic objects, a file geodatabase representing a cartographic reconstruction of natural resource use in 1952 was created in the ArcGIS software environment. This provides a retrospective snapshot of the natural state of coastal geosystems of the lake and makes it possible to establish physical and geographical features of past economic use of the territory. Based on the elevations and contours of the topographic basis at the scale of 1 : 50 000 based on a 1952 survey, a digital terrain model was compiled, as was a map of geomorphological structure of the study area. By combining the reconstruction with modern digital layers, the dynamics of waterlogging and flooding of the delta territory as a result of long-term level regulation of Lake Baikal were established, and a spatiotemporal assessment of the dynamics of individual objects of natural resource use was performed. By physical and geographical location within the estuary and by mode of traditional natural resource use, all settlements were divided into three groups: coastal, island, and estuarine. A register of settlements with a breakdown by area, number of households, and population size has been compiled. A cartographic assessment of the dynamics of the water surface area was used to identify waterlogged and flooded areas, stable areas, abandoned settlements, abandoned socioeconomic facilities, and abandoned arable land. Maps of the dynamics of the water surface and natural resource use were compiled. [ABSTRACT FROM AUTHOR]

Today's technologies make it possible to capture certain phenomena that were very difficult or impossible to observe in terms of classical measurements. One of them is the so-called sinking of embankments. It is common in mining areas. It consists in the additional subsidence of the embankments into the ground, above the value of the lowering of the adjacent area. It takes place primarily in the zone of horizontal tensile deformations. The paper presents the results of comparative DTM (Digital Terrain Model) analyzes from 2001, 2014, 2018 and 2021. Their aim was to assess the usefulness of DTM data for monitoring the additional sinking of the communication embankment on the example of the northern bypass of Bytom. The authors analyzed digital terrain models generated in the process of rasterization of data from ALS (Airborn Laser Scanning). [ABSTRACT FROM AUTHOR]

The estimation of Soil Organic Carbon (SOC), Total Nitrogen (TN), and Soil Water Storage (SWS) is crucial in comprehending ecosystem services and environmental sustainability. It plays a crucial role in guiding sustainable restoration strategies and supporting the long-term health of post-mining sites. Remote sensing technology provides valuable tools for modelling and mapping soil properties in reclaimed post-mining sites efficiently and cost-effectively. This study aimed to utilize remote sensing data to estimate SOC, TN, and SWS in a reclaimed post-mining site. Field data was collected from 130 research plots to obtain reference data for SOC, TN, and SWS from the Sonica hard coal post-mine spoil heap. Remote sensing data were: airborne laser scanning (ALS) point clouds and Planets cope satellite imageries. Generalized Additive Models (GAM) were used to develop predictive models. Wall-to-wall predictions of analyzed variables were performed. The results identified topographic and remote sensing indicators that significantly influence SOC, TN, and SWS. Digital Terrain Model (DTM), aspect, and blue spectral band are variables that explain SOC storage, with a significant influence of DTM, ranging from −8 to 18 Mg ha−1. TN was explained by DTM, Canopy Height Model (CHM), blue and Near Infrared (NIR) spectral bands, and Normalized Difference Vegetation Index (NDVI), mainly influenced by NIR and NDVI, ranging from −1.1 to 0.8 and −0.9 to 1.4 Mg ha−1, respectively. The values of Topographic Wetness Index (TWI), aspect, CHM, blue and NIR spectral bands explained SWS, highlighting their importance in assessing soil water dynamics in post-mining landscapes, with TWI and CHM being particularly influential, ranging from −2 to 5.1 and −6 to 2 mm, respectively. However, caution is advised when predicting SOC and TN using remote sensing in post-mining sites due to geogenic carbon considerations.

Inconsistent geoid heights may introduce uncertainties that limit its use in applications. Consequently, the scientific community has been collaborating to determine more rigorous geoid models in order to improve the accuracy of orthometric heights estimated with height transformation from the Global Navigation Satellite System (GNSS) positioning techniques. In this regard, this study evaluated the influence of different parameters (such as mass density, topographic models, geopotential models, and Stokes kernel modifications) used in the determination of a gravimetric geoid model through the remove-compute-restore (RCR) technique to contribute to these efforts. As a result of the analyses, it was determined that the Stokes kernel modification type and the global geopotential model selection significantly influence the improvement of geoid models in the study area (state of São Paulo, Brazil). However, the resolution of digital terrain models and the values of the lateral topographic density have less of an impact on the model's accuracy when determining the geoid. In order to acquire findings that are consistent with geoid heights estimated by each determination technique, it is necessary to conduct a rigorous study of the employed data and computation parameters as a preliminary process. [ABSTRACT FROM AUTHOR]

Purpose. Development of a new approach to improving the accuracy of orientation based on radar reflections from local objects and a digital terrain model. Methodology. The research is based on the theory of radiation, reflection and reception of radar signals. Statistical analysis of a large volume of recorded signals establishes causal relationships between the appearance of “false” reflections formed by the back lobe of the radiation pattern and to develop a computational algorithm for their suppression. Findings. A method and software that allows detecting and suppressing “false” reflections formed by the rear lobe of the antenna pattern without distorting the reflections created by the main lobe. For this purpose, a criterion has been developed, determined by the ratio of the amplitude of the signal received by the front lobe to the amplitude of the signal recorded by the rear lobe. The criterion allows eliminating the “false” signals without having a priori information about the real radiation pattern using a regulator for the reduction of phantom reflections to an average noise level. Originality. For the first time, suppression has been carried out of “false” reflections without having a priori information about the real radiation pattern of the radar station antenna, as well as elimination of the loss of informativeness of the real reflection formed by the main lobe. Practical value. A method is suggested of radar immunity of radar stations is noise immunity due to “false” reflections. The potential of the method and the capabilities of the developed computer program determines the relevance of their capabilities for use by all radar stations at various frequencies, azimuths, ranges and terrain features. [ABSTRACT FROM AUTHOR]

Coastal erosion is a pervasive global phenomenon, exemplified by the Hel Peninsula situated in the Gulf of Gdańsk, the southern Baltic Sea. The geological constitution of the Hel Peninsula, characterized by sandy and loosely consolidated material, predisposes its coastal zones to continual morphological changes. The peninsula's limited width and elevation exacerbate shoreline erosion, particularly during periods of heightened storm activity. This study scrutinizes the effectiveness of coastal nourishment interventions, with a specific focus on segments influenced by the Władysławowo port and the Kuźnica vicinity, over several years. This specific section of the coast serves as a significant case study due to its role as a transit zone for sand transport along the whole peninsula. Protective measures, including shore nourishments and coastal groynes, aim to mitigate erosion impacts. Utilizing Airborne Laser Scanning (ALS) data spanning from 2008 to 2022, erosion dynamics were analyzed. The analysis reveals significant erosion patterns coinciding with the frequency and volume of nourishment material deposition, particularly evident in heavily nourished areas proximate to Władysławowo and Kuźnica. Despite persistent monitoring endeavors, persistent erosive trends pose imminent threats to Kuźnica's infrastructure, necessitating further research into the efficacy of implemented coastal protection measures. [ABSTRACT FROM AUTHOR]

Due to the continuous increase in the volume of spatially located information, the current requirements imposed on the Spatial Information System (SIS) concern increasing data mining capabilities. Modern measurement systems, based on devices which enable the automatic recording of observation results on a mass scale (LiDAR—Light Detection and Ranging, MBES—Multi Beam Echo Sounder, etc.), allow for a very large volume of information on the surface to be measured and acquired in a relatively short time. One of the methods to reduce the volume of data enabling the generation of a model surface is to convert unevenly distributed measurement points into a regular network of squares (GRID). However, the generation of a complete grid is not always possible. In the measurement spectrum, there may be areas where measurement points have not been recorded. Measurement points can also be eliminated by either filtering the erroneously recorded data or eliminating the measured vegetation or the utilities in the area. To address these problems, the current article proposes a method for complementing the missing internal nodes in a regular network of squares using polynomial interpolation algorithms. Moreover, the paper demonstrates the possibilities of using the presented method for adding additional points between the already existing nodes of the network of squares. The application of the methodology presented in this article enables the effective elimination of (or a reduction in) the gaps in the GRID structure, which, in turn, allows such a network of squares to be used to generate a more accurate Digital Terrain Model. [ABSTRACT FROM AUTHOR]

Shelf-incised valleys, carved by ancient rivers and estuaries during periods of continental shelf exposure, were primarily shaped by base-level fluctuations in the Quaternary. These geological features are prevalent in the western South Atlantic shelf but often go unnoticed as key indicators of the region's evolution. This research unveils the outcomes of a comprehensive investigation that combined sub-bottom profiling (SBP) surveys, historical bathymetric data, and the application of Geographic Information System (GIS) tools to extract drainage patterns within the Santos Basin, a significant sedimentary basin in Brazil.The study provides an unprecedented overview of the interconnected network of the ancient drainage system across the continental shelf, totalizing 91 thalwegs associated with 80 incised valleys in the study area, ranging in length from 250 m to 148 km. This connection between the mapped incised valleys and the upper slope evolved through three distinct phases during the Late Pleistocene. The first phase (Phase A) was characterized by the formation and subsequent destruction of incised valleys, driven by high-frequency sea-level oscillations during MIS5. The second phase (Phase B, with subphases B1 and B2) witnessed the construction of incised valleys during a period of forced regression at MIS4 and MIS3. The final phase (Phase C) marked the establishment of fully developed valleys, connecting them to upper slope canyons during the Last Glacial Maximum (LGM). The correlation of these sea-level changes with the presence of underfilled incised valleys in the Santos Basin shelf represents a significant breakthrough in understanding the geological history of the western South Atlantic shelf. These valleys typically eroded until reaching depths near the transient base level, offering a valuable glimpse into the geological chronicles of sea-level fluctuations over time. [ABSTRACT FROM AUTHOR]

In the spacious inundation area on the left bank of the lower course of the Sava River in Serbia, there is an abandoned meander Special Nature Reserve, "Obedska bara", which represents a very important floodplain in this part of Europe. This area is characterized by an exceptional wealth of biodiversity with a significant presence of rare and endangered species of national and international importance. Hydrological conditions in the mentioned area were analyzed from the aspect of surface water movement in nature and conditions altered by human factors (after the construction of the road network, canals, etc.). The movement of surface water, i.e., the filling and emptying of the investigated area, parallel to the water level of the Sava River, is shown using a digital terrain model. Our simulation of the change in surface water level within the studied area included the display of underwater areas, both with the formation of a flood wave (i.e., increasing water level of the Sava) and with the outflow of water from the pond when the water level in the Sava was reduced in both scenarios (natural and conditions altered by human factors). GIS and terrain digitalization were used for geospatial and hydrological analyses and, based on this, maps that display endangered areas could be made. The obtained results show that the largest human impact was recorded at the water level of the Sava River 74 m above sea level. The aforementioned water regime changes were shown to negatively affect dominant vegetation, such as pedunculate oak and ash. [ABSTRACT FROM AUTHOR]

Open‐source, satellite‐based digital elevation models (DEMs) are widely used for flood modeling. However, studies on effectiveness of these DEMs in depicting local‐level flood processes are limited. This study generated a high‐resolution digital terrain model (DTM) based on unmanned aerial vehicle (UAV) photogrammetry and used in a two‐dimensional (2D) hydrodynamic model (HEC‐RAS) to simulate the flood processes in a floodplain environment of the Jamuna River in northern Bangladesh. The effectiveness of a few satellite‐based DEMs was also compared with this DTM by using the DEMs in the same hydrodynamic model. Field data for two flood seasons were collected to develop the model. The results indicate that the 2D model with UAV‐based DTM provides the flood parameters, such as flood arrival time, depth, duration and extent, better than those from the satellite‐based DEMs. Of the open‐source DEMs, the FABDEM and the WorldDEM™ have the least errors and provide better results compared to the SRTM30, ALOS PALSAR, and ASTER DEMs. The UAV technique with ground control points and field measurements for the tree‐canopy and water areas is very useful in generating a fit‐for‐purpose DTM. The findings of this study would be useful for terrain generation and DEM selection for local‐level flood modeling elsewhere. [ABSTRACT FROM AUTHOR]

Abstract Open‐source, satellite‐based digital elevation models (DEMs) are widely used for flood modeling. However, studies on effectiveness of these DEMs in depicting local‐level flood processes are limited. This study generated a high‐resolution digital terrain model (DTM) based on unmanned aerial vehicle (UAV) photogrammetry and used in a two‐dimensional (2D) hydrodynamic model (HEC‐RAS) to simulate the flood processes in a floodplain environment of the Jamuna River in northern Bangladesh. The effectiveness of a few satellite‐based DEMs was also compared with this DTM by using the DEMs in the same hydrodynamic model. Field data for two flood seasons were collected to develop the model. The results indicate that the 2D model with UAV‐based DTM provides the flood parameters, such as flood arrival time, depth, duration and extent, better than those from the satellite‐based DEMs. Of the open‐source DEMs, the FABDEM and the WorldDEM™ have the least errors and provide better results compared to the SRTM30, ALOS PALSAR, and ASTER DEMs. The UAV technique with ground control points and field measurements for the tree‐canopy and water areas is very useful in generating a fit‐for‐purpose DTM. The findings of this study would be useful for terrain generation and DEM selection for local‐level flood modeling elsewhere.

ABSTRACTWe study the marine terraces of the southern side of the Sibari Plain in Northern Calabria (Italy) through the use of traditional and quantitative analyses of the Digital Terrain Model (DTM). The main aim of the present work consists in the extensive use of GIS tools that were never used before in the area, and in checking the applicability of this procedure. The terraced surfaces identified using photo interpretation and those recognized semi-automatically through the GIS tools were compared to finally produce a consensus map. In the final map, we identified 272 terraced surfaces and 62 morphological features associated with inner margins (i.e. paleoshorelines). The main map shows a well-developed flight of seven orders of marine terraces with elevation ranging from 45 to 360 m asl and age ranging from Marine Isotope Stage (MIS) 5a to 11.

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods. [ABSTRACT FROM AUTHOR]

We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High‐Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co‐aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359. Key Points: We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on MarsThe resultant 50 cm/pixel digital terrain model mosaic demonstrates significant improvements in effective resolution and artifact elimination compared to the publicly available Mars 2020 Terrain Relative Navigation TRN High‐Resolution Imaging Science Experiment digital terrain modelThe resultant digital terrain model mosaic has been made publicly available at http://dx.doi.org/10.17169/refubium-38359 [ABSTRACT FROM AUTHOR]

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim. [ABSTRACT FROM AUTHOR]

This paper proposes a general methodological approach to hydrological modeling for determining the areas of flooded land in the plain part of the Dniester riverbed, the second largest river in Ukraine. The purpose of the study is the selection of parameters for modeling flooded zones in the plain section of the Dniester riverbed, taking into account the rising water level caused by the freshet that occurred in the western part of Ukraine on 22–24 June 2020. The proposed study examines the workflow of hydrological modeling of the flooded land zone and the main components of this scheme: the construction of the DTM, considering the roughness of the riparian territory by Manning's coefficients, and indications of water rise. In the experiment, the influence of DTM reproduction resolution and Manning coefficients was analyzed, and their optimal values were selected, which allowed obtaining the parameters of hydrological modeling with a higher probability. The identified flooding areas were tested using a high-resolution space image during the flood in June 2020. The distance between the profiles affects not only the value of the modeling area but also their detail. The accuracy of the modeled flooded area is 5.1% for a 5 m interval between the profiles, 6.9% for 50 m, 8.2% for 100 m, and 10.8% for 200 m. These results allow determining the degree of influence of the distance between intervals on the modeling accuracy. Using different values of Manning's coefficients for individual sections with different bedding surfaces and the selected spacing between profiles, which was 50 m, the accuracy of the modeling was investigated. After the modelling, the simulated flood areas were obtained in vector form, which allows for determining their areas and comparing them with the test flooded area. In the presented research, the RMSE of determining the flooded areas is about 5%. The test area of 600.6 hectares was determined with an accuracy of 0.8%. [ABSTRACT FROM AUTHOR]

The combination of Remote Sensing and Deep Learning (DL) has brought about a revolution in converting digital surface models (DSMs) to digital terrain models (DTMs). DTMs are used in various fields, including environmental management, where they provide crucial topographical data to accurately model water flow and identify flood-prone areas. However, current DL-based methods require intensive data processing, limiting their efficiency and real-time use. To address these challenges, we have developed an innovative method that incorporates a physically informed autoencoder, embedding physical constraints to refine the extraction process. Our approach utilizes a normalized DSM (nDSM), which is updated by the autoencoder to enable DTM generation by defining the DTM as the difference between the DSM input and the updated nDSM. This approach reduces sensitivity to topographical variations, improving the model’s generalizability. Furthermore, our framework innovates by using subtractive skip connections instead of traditional concatenative ones, improving the network’s flexibility to adapt to terrain variations and significantly enhancing performance across diverse environments. Our novel approach demonstrates superior performance and adaptability compared to other versions of autoencoders across ten diverse datasets, including urban areas, mountainous regions, predominantly vegetation-covered landscapes, and a combination of these environments.

Due to the continuous increase in the volume of spatially located information, the current requirements imposed on the Spatial Information System (SIS) concern increasing data mining capabilities. Modern measurement systems, based on devices which enable the automatic recording of observation results on a mass scale (LiDAR—Light Detection and Ranging, MBES—Multi Beam Echo Sounder, etc.), allow for a very large volume of information on the surface to be measured and acquired in a relatively short time. One of the methods to reduce the volume of data enabling the generation of a model surface is to convert unevenly distributed measurement points into a regular network of squares (GRID). However, the generation of a complete grid is not always possible. In the measurement spectrum, there may be areas where measurement points have not been recorded. Measurement points can also be eliminated by either filtering the erroneously recorded data or eliminating the measured vegetation or the utilities in the area. To address these problems, the current article proposes a method for complementing the missing internal nodes in a regular network of squares using polynomial interpolation algorithms. Moreover, the paper demonstrates the possibilities of using the presented method for adding additional points between the already existing nodes of the network of squares. The application of the methodology presented in this article enables the effective elimination of (or a reduction in) the gaps in the GRID structure, which, in turn, allows such a network of squares to be used to generate a more accurate Digital Terrain Model.

Abstract We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High‐Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co‐aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359.

Communicating environmental change and mitigation scenarios to stakeholders and decision-makers can be challenging. Immersive environments offer an innovative approach for knowledge transfer, allowing science-based scenarios to be discussed interactively. The use of such environments is particularly helpful for the analysis of large, multi-component geospatial datasets, as commonly employed in the classification of ecosystems. Virtual environments can play an important role in conveying and discussing the findings gathered from these geomorphometric datasets. However, textured meshes and point clouds are not always well suited for direct import to a virtual reality or the creation of a truly immersive environment, and often result in geometrical artifacts, which can be misinterpreted during the import to a game engine. Such technical hurdles may lead to viewers rejecting the experience altogether, failing to achieve a higher educational purpose. In this study, we apply an asset-based approach to create an immersive virtual representation of a coastal environment. The focus hereby is on the coastal vegetation and changes in species distribution, which could potentially be triggered by the impact of climate change. We present an easy-to-use blueprint for the game engine EPIC Unreal Engine 5. In contrast to traditional virtual reality environments, which use static textured mesh data derived from photogrammetry, this asset-based approach enables the use of dynamic and physical properties (e.g. vegetation moving due to wind or waves), which makes the virtual environment more immersive. This will help to stimulate understanding and discussion amongst different stakeholders, and will also help to foster inclusion in earth- and environmental science education.

Digital terrain model (DTM) creation is a modeling process that represents the Earth's surface. An aptly designed DTM generation method tailored for intended study can significantly streamline ensuing processes and assist in managing errors and uncertainties, particularly in large-area projects. However, existing methods often exhibit inconsistent and inexplicable results, struggle to clearly define what an object is, and often fail to filter large objects due to their locally confined operations. We introduce a new DTM generation method that performs object-based ground filtering, which is particularly beneficial for urban topography. This method defines objects as areas fully enclosed by steep slopes and grounds as smoothly connected areas, enabling reliable "object-based" segmentation and filtering, extending beyond the local context. Our primary operation, controlled by a slope threshold parameter, simplifies tuning and ensures predictable results, thereby reducing uncertainties in large-area modeling. Uniquely, our method considers surface water bodies in modeling and treats connected artificial terrains (e.g., overpasses) as ground. This contrasts with conventional methods, which often create noise near water bodies and behave inconsistently around overpasses and bridges, making our approach particularly beneficial for large-area 3D urban mapping. Examined on extensive and diverse datasets, our method offers unique features and high accuracy, and we have thoroughly assessed potential artifacts to guide potential users. [ABSTRACT FROM AUTHOR]

The purpose of this work is to describe the realization of a photogrammetric mapping work, i.e. the creation of a high-resolution orthophotoplan mosaic, as a support in the inventory of land parcels belonging to the public domain of a territorial administrative unit in the rural area, subsequent registration in the Integrated Cadaster and Land Registry System, with all related technical-legal aspects. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production. [ABSTRACT FROM AUTHOR]

The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000-2020+ Landsat data, 2017-2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. Sentinel-2 and Landsat reflectance values were aggregated into four quarterly averages approximating the four seasons common in Europe (winter, spring, summer and autumn), as well as the 25th and 75th percentile, in order to retain intra-seasonal variance. Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. An accuracy assessment shows TMWM performs relatively better in Southern Europe and lower in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. We quantify the usability of the different component data sets for spatiotemporal machine learning tasks with a series of land cover classification experiments, which show that models utilizing the full feature space (30 m DTM, 30 m Landsat, 30 m and 10 m Sentinel-2) yield the highest land cover classification accuracy, with different data sets improving the results for different land cover classes. The data sets presented in the article are part of the EcoDataCube platform, which also hosts open vegetation, soil, and land use/land cover (LULC) maps created. All data sets are available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through SpatioTemporal Asset Catalog (STAC) and the EcoDataCube data portal. [ABSTRACT FROM AUTHOR]

Watershed planning is often based on the analysis of morphometric parameters, especially in poorly gauged or ungauged basins. These physiographic parameters have, in fact, a main role in water runoff. In many arid countries such as Morocco, there is a significant need for morphometric studies of watersheds to initiate integrated water resources management. For this purpose, we have carried out the watersheds delineation and morphometric analyses, using the Digital Terrain Model (DTM) and the Geographic Information System (GIS). We have applied this approach based on remote sensing and GIS in four sub-basins of the right bank of the Tensift watershed (Bourrous, Al Wiza, El Hallouf and Jamala). The shape indexes of Gravelius and Horton reveal elongated shapes of the four watersheds. In addition, the maximum slope and the drainage density do not exceed 27.15° and 1 Km/Km2 respectively. The sub-basins do not have a very dense hydrographic network and the Strahler's drainage order is not very high (up to 5). The relief is not very high and do not reach 1000 m. These physiographic conditions do not allow a rapid runoff. The concentration times are precisely quite high (7 to 12 hours for watersheds of 161 to 401 km²). The use of a sufficiently fine DTM resolution and an appropriate GIS software would allow this kind of study to be very useful for effective watershed management.

This paper considers an experimental study of the layout of an active-pulse television measuring system in the problem of assessing the accuracy of measuring the distance to objects using the depth maps. The main technical characteristics and structure of the active-pulse television measuring system layout are described, the description of the multi-zone ranging method used in the experiment is given. The field tests were carried out using a system for terrain orthophotomaps construction by an unmanned aerial vehicle and a geodetic measuring instrument, which is a reference for building a terrain plan and fixing distances between objects on the ground. The technique of carrying out aerial work is described to obtain the necessary data array, on which a digital model and an orthophotomap of the area were subsequently built. Conclusions are drawn about the accuracy of digital terrain models built based on the results of aerial photography from an unmanned aerial vehicle with a geodetic receiver on board and the applicability of these data as reference data for testing a prototype of an active-pulse television measuring system.

We demonstrate the creation of a large area of high-resolution (260 × 209 km2 at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang'E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang'E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive's guest storage facility. [ABSTRACT FROM AUTHOR]

Aerial laser scanning technology has excellent potential in landscape management and forestry. Due to its specific characteristics, the application of this type of data is the subject of intensive research, with the search for new areas of application. This work aims to identify the boundaries of forest stands, and forest patches on non-forest land. The research objectives cover the diversity of conditions in the forest landscapes of Slovakia, with its high variability of tree species composition (coniferous, mixed, deciduous stands), age, height, and stand density. A semi-automatic procedure was designed and verified (consisting of the creation of a digital terrain model, a digital surface model, and the identification of peaks and contours of tree crowns), which allows after identification of homogeneous areas of forest stands and/or forest patches (areas covered with trees species canopy) with selected parameters (height, crown size, gap size), with high accuracy. The applicability of the proposed procedure increases the use of freely available ALS data (provided by the Office of Geodesy, Cartography, and Cadastre of the Slovak Republic) and freely distributable software tools (QGIS, CloudCompare). [ABSTRACT FROM AUTHOR]

Multi‐temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)‐borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi‐temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi‐temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV‐borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf‐off conditions (e.g., cross‐flight strategy) turned out to be sufficient for our geomorphic research purposes. [ABSTRACT FROM AUTHOR]

ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes. [ABSTRACT FROM AUTHOR]

Imaging and measuring the Earth's relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing. [ABSTRACT FROM AUTHOR]

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%. [ABSTRACT FROM AUTHOR]

The article presents the technique for geoinformation modeling of a site of a river valley with an insufficient amount of hydrological data. The technique will make it possible to carry out a forecast of hydrological phenomena in order to prevent the flooding of adjacent territories by flood waters. The study is based on the use of Earth remote sensing (RS) data and the use of geoinformation technologies (GIS). The sequence of the work is briefly described. The study was carried out on the example of a site of the valley of the Yesil (Ishim) river, located near Petropavlovsk (North Kazakhstan region, Republic of Kazakhstan). On the basis of remote sensing data and GIS technologies, a digital relief model of the studied section of the river valley was developed. The initial data were SRTM (Shuttle Radar Topography Mission) images. The modeling of the scenario of possible flooding of adjacent territories by flood waters has been carried out. Modeling and mapping of flooding carried out for the flood peak. Modeling and construction of a flood map were carried out for the flood peak. An approach is proposed that makes it possible to perform a more accurate assessment of the boundaries of flooding without the procedure for calculating the hydrological characteristics of the river during the flood period and the absence of hydrometric observation data. Recommendations are given for the prevention of flood events, measures to protect adjacent lands from possible flooding. The obtained cartographic materials can be useful for planning and carrying out measures to prevent emergency situations related to floods on the river, reduce the risk of flooding of adjacent areas, and territorial design. [ABSTRACT FROM AUTHOR]

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The uncertainty that comes with planning, constructing, and maintaining buildings is a constant issue for architects and civil engineers. As topography is the framework that unites architecture and landscape, the design and planning projects heavily rely on a range of monitoring, surveying methods and comprehensive field data. Along with the traditional topo-geodetic instrumentation used in land and construction surveying, unmanned aerial vehicles equipped with digital cameras and structure from motion software have been increasingly used recently in a variety of fields to create high-resolution digital elevation models. Despite this widespread use, in the majority of surveying projects it is considered that the topographic representations produced through this technology is inferior to that obtained with surveys conducted using conventional methods, along with other constraints imposed by legislation, environment and weather conditions. While certain limitations of unmanned aerial vehicle (UAV) systems are challenging, their advantage for gathering data from a different perspective and the generated outputs have the potential to significantly advance the construction industry. The present article provides an overview of the usefulness of budget UAV systems in developing a methodology that accompanies the conventional survey process for civil engineering applications. Thus, along with the established survey for cadastral and technical documentations necessary for the architectural process, a complementary UAV survey was developed, with subsequent spatial analysis in a geographic information system (GIS), in order to expand the array of deliverables. These include useful orthophoto map, larger-scale and denser representations of the topography, digital surface and terrain models, slope, aspect and solar radiation maps which will offer helpful information and instructions at the start of the construction planning process. The methodology contains two case studies with different degrees of terrain and vegetation challenges, and also presents an accuracy assessment and overall benefits discussion regarding the UAV implementation.

The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000–2020+ Landsat data, 2017–2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. Sentinel-2 and Landsat reflectance values were aggregated into four quarterly averages approximating the four seasons common in Europe (winter, spring, summer and autumn), as well as the 25th and 75th percentile, in order to retain intra-seasonal variance. Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. An accuracy assessment shows TMWM performs relatively better in Southern Europe and lower in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. We quantify the usability of the different component data sets for spatiotemporal machine learning tasks with a series of land cover classification experiments, which show that models utilizing the full feature space (30 m DTM, 30 m Landsat, 30 m and 10 m Sentinel-2) yield the highest land cover classification accuracy, with different data sets improving the results for different land cover classes. The data sets presented in the article are part of the EcoDataCube platform, which also hosts open vegetation, soil, and land use/land cover (LULC) maps created. All data sets are available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through SpatioTemporal Asset Catalog (STAC) and the EcoDataCube data portal.

This contribution deals with surveyor activities that are associated with the measurement of watercourses and their closest terrain. It clarifies the specifics and complications of the solutions of the complex tasks of water management in geodetic practice. The article is devoted to activities that are directly connected with the realization of measuring, used technologies, and interpretation results’ outputs in digital form.

In the spacious inundation area on the left bank of the lower course of the Sava River in Serbia, there is an abandoned meander Special Nature Reserve, “Obedska bara”, which represents a very important floodplain in this part of Europe. This area is characterized by an exceptional wealth of biodiversity with a significant presence of rare and endangered species of national and international importance. Hydrological conditions in the mentioned area were analyzed from the aspect of surface water movement in nature and conditions altered by human factors (after the construction of the road network, canals, etc.). The movement of surface water, i.e., the filling and emptying of the investigated area, parallel to the water level of the Sava River, is shown using a digital terrain model. Our simulation of the change in surface water level within the studied area included the display of underwater areas, both with the formation of a flood wave (i.e., increasing water level of the Sava) and with the outflow of water from the pond when the water level in the Sava was reduced in both scenarios (natural and conditions altered by human factors). GIS and terrain digitalization were used for geospatial and hydrological analyses and, based on this, maps that display endangered areas could be made. The obtained results show that the largest human impact was recorded at the water level of the Sava River 74 m above sea level. The aforementioned water regime changes were shown to negatively affect dominant vegetation, such as pedunculate oak and ash.