Geospatial Data Processing Research Articles

Automated geovisualization of flood disaster impacts in the global South cities with open geospatial data sets and ICEYE SAR flood data

Flooding is a highly destructive natural disaster affecting millions of people annually, causing substantial global economic losses. Especially in the Global South, vulnerable populations with limited resources for flood protection and recovery suffer the most. However, effective flood impact analysis and standardized geospatial data processing and visualization is lacking, hindering effective disaster risk reduction. This study employs Python to develop a standardized, automated analysis and visualization model to address flood impacts after a high magnitude event anywhere in the world by using global geospatial data sets.In order to test its applicability two relevant cases, with different locations and scales, were selected, (Bangkok, Thailand, and Tula de Allende, Mexico). In both cases the automated flood model efficiently processed flood extent and depth data from ICEYE SAR images, added the population estimates from the German Aerospace Center, and key infrastructure elements from OpenStreetMap. The output of the automated process is presented as web-based interactive maps, offering insights into flooding severity and impacts with minimal user input. This quick approach (processing time between 41 s and 10,5 min) facilitates timely responses by first responders, with great potential to aid and improve the efficiency of international humanitarian efforts.By leveraging global high-resolution geospatial data for local-level analysis, this study demonstrates the versatility and time-saving benefits of this automated analysis and visualization. Automated models standardize results, minimizing human errors, and enabling consistent historical flood data comparison. Even though the databases are gathered from trusted sources, additional evaluation of uncertainties of the results with field data should be considered in further development of this tool. Nevertheless, this research highlights the worldwide potential, and especially the Global South, of automated global geospatial analysis to improve disaster impact reduction by enhancing efficient response efforts.

Encoding and operation scheme for the rhombic triacontahedron aperture 4 hexagonal discrete global grid system

ABSTRACT Discrete Global Grid System (DGGS) is a hierarchical structure with seamless global coverage, supporting processing and analysis of heterogeneous geospatial data. Compared with the most commonly used icosahedron, the rhombic triacontahedron (RT) approximates Earth better and can improve the accuracy of data modeling and expression. However, current RT DGGS studies have adopted a single-resolution integer coordinate scheme within a single surface that cannot achieve multilevel grid analysis. To this end, three adjacent surfaces are combined and a three-axis integer coordinate system is established to reduce the number of times crossing the surface. Then, the principle for partitioning aperture 4 hexagonal grids and a quadtree hierarchical encoding scheme are proposed. Further, this study establishes a fast transformation between the code and three-axis coordinates, implements hierarchical and neighbor code operations, and designs a transformation between the code and geographic coordinates. Experimental results indicate that, compared with the icosahedral hexagonal DGGS, the proposed scheme has a significant efficiency advantage in the transformation between the code and geographic coordinates, while the neighbor code operation efficiency can reach 40.97 times that of HLQT and 4.35 times that of HHUT. The proposed scheme provides a more suitable framework for organizing and analyzing global geospatial data.

International exchange of geospatial information and cooperation in the field of land castras

It was determined that the modern development of geographic information systems (GIS) and the growing need for international exchange of geospatial information (GI) emphasize the importance of this area for solving global challenges. The diversity of standards and methodologies in different countries creates specific barriers to practical cooperation. However, introducing the latest technologies, such as cloud computing and artificial intelligence, opens up new opportunities for exchanging and using AI at the international level. The research focuses on analyzing existing GI exchange practices, examining issues that hinder cooperation, examining cases of successful international exchange, and identifying factors that facilitate or hinder effective exchange. The importance of developing international cooperation in the cadastre of natural resources is emphasized by the unification of practices and standards and the development of a common regulatory framework, which promotes a global approach to solving cadastral issues. The development of such infrastructures as the US National Spatial Data Infrastructure (NSDI) is a vivid example of the successful implementation of integration and access to geospatial information that supports decision-making at the government level. Similar projects implemented in other countries and regions, including the Global Spatial Data Infrastructure (GSDI) and others, testify to the growing importance of joint efforts in this area. A vital aspect of the research is the analysis of challenges related to the international exchange of GIs and the search for ways to overcome them, in particular through the standardization of methods of collection, processing, and presentation of geospatial data, the use of open standards provided by the OGC consortium is defined as the basis for the development of web applications, that support sharing and collaboration in the field of GI. Ultimately, the study emphasizes the importance of continuous development and maintenance of spatial data infrastructures both for solving specific institutional tasks and for the general improvement of the availability and efficiency of geospatial information use, updating the technological base, regular data updates, and integration with modern information technologies, such as cloud computing and artificial intelligence are identified as critical elements of successful international exchange of geospatial information. Keywords: geospatial information, spatial data infrastructure, land cadastre, unified exchange format, GeoViewer, geoportal.

GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system

Abstract. A geospatial data processing tool, GEO4PALM, has been developed to generate geospatial static input for the Parallelized Large-Eddy Simulation (PALM) model system. PALM is a community-driven large-eddy simulation model for atmospheric and environmental research. Throughout PALM's 20-year development, research interests have been increasing in its application to realistic conditions, especially for urban areas. For such applications, geospatial static input is essential. Although abundant geospatial data are accessible worldwide, geospatial data availability and quality are highly variable and inconsistent. Currently, the geospatial static input generation tools in the PALM community heavily rely on users for data acquisition and pre-processing. New PALM users face large obstacles, including significant time commitments, to gain the knowledge needed to be able to pre-process geospatial data for PALM. Expertise beyond atmospheric and environmental research is frequently needed to understand the data sets required by PALM. Here, we present GEO4PALM, which is a free and open-source tool. GEO4PALM helps users generate PALM static input files with a simple, homogenised, and standardised process. GEO4PALM is compatible with geospatial data obtained from any source, provided that the data sets comply with standard geo-information formats. Users can either provide existing geospatial data sets or use the embedded data interfaces to download geo-information data from free online sources for any global geographic area of interest. All online data sets incorporated in GEO4PALM are globally available, with several data sets having the finest resolution of 1 m. In addition, GEO4PALM provides a graphical user interface (GUI) for PALM domain configuration and visualisation. Two application examples demonstrate successful PALM simulations driven by geospatial input generated by GEO4PALM using different geospatial data sources for Berlin, Germany, and Ōtautahi / Christchurch, New Zealand. GEO4PALM provides an easy and efficient way for PALM users to configure and conduct PALM simulations for applications and investigations such as urban heat island effects, air pollution dispersion, renewable energy resourcing, and weather-related hazard forecasting. The wide applicability of GEO4PALM makes PALM more accessible to a wider user base in the scientific community.

PelaSIG, a QGIS plugin for marine megafauna census: application to the aerial ACCOBAMS Survey Initiative dataset

The distribution of wild animals and their monitoring over large areas raises many logistical and technical difficulties that hinder the collection of observation data. The use of Geographic Information Systems (GIS) has increased significantly in recent years. QGIS, an open-source GIS software dedicated to the processing of geospatial data, enables the development of dedicated plugins for specific workflows. The open-source PelaSIG plugin has been developed in Python for QGIS 3 to facilitate and standardise the different steps before and after distance sampling surveys. It brings together a set of tools for survey preparation, automatic data checking, visualisation and presentation of survey effort and sightings to provide an adapted workflow. This plugin is currently designed to process dedicated aerial datasets collected with the SAMMOA software during marine megafauna surveys (i.e., marine mammals, seabirds, elasmobranchs, sea turtles, etc.). Here, we first describe the different tools already available, and then, we present an application with the dataset from the aerial survey of the ACCOBAMS Survey Initiative (ASI) conducted in 2018 over the Mediterranean Sea and using a multi-target protocol.

Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches

In recent years, the concept of smart cities has become increasingly important in the pursuit of sustainable development goals. In general, common urban challenges have been addressed through smart-city services, and new perspectives for more sustainable cities have emerged. To realize the full potential of such smart urban environments, geospatial approaches have been used as a focal point, offering a plethora of applications that contribute to a better understanding of urban challenges and innovation potentials. Nevertheless, although significant progress has been made, different problems may arise when the available technologies and resources are not understood or even when their potentialities are not properly capitalized. This article reviews the state of the art in the field, highlighting success cases and remaining challenges in exploiting geospatial data-driven strategies, particularly when leveraging geographic information systems, satellites, and distributed sensors to produce and process geospatial data and datasets in urban scenarios. Moreover, a more organized perspective of the area is provided in this article, as well as future development trends, supporting new research efforts in this area when empowering smart cities for a more sustainable future.

Clast shape-fabric analysis: A comprehensive and efficient methodology to measure particle-orientation data in solid and loose volcaniclastic deposits

Fabric analysis is essential for understanding the evolution of volcaniclastic deposits. Here we present a comprehensive and efficient methodology, called “Clast shape-fabric analysis,” which is part of the Quantitative Textural Analysis (QTA). This methodology combines high-resolution image analysis techniques with geospatial data processing tools.The fabric of a deposit refers to the three-dimensional orientation of the particles with respect to space, where the degree of iso-orientation of the major axes of the particles is taken into account. The process begins with the collection of oriented samples in the field. Then, in the laboratory, the samples are processed to obtain high-resolution images. The final stage involves the analysis of these images using the FabricS program, which combines image processing techniques and circular statistics.An application of the method was made at the Joya Honda Maar in Mexico, where shape-fabric analysis was used to identify the emission centers of pyroclastic materials.In summary, the “Clast shape-fabric analysis” is a reliable, low-cost and high-potential methodology that can be applied in several geoscientific disciplines and other areas of scientific research.•New Methodology for shape-fabric analysis is presented.•The methodology involves field work, laboratory work and image analysis.•Identification of particle orientations in volcaniclastic deposits.

Advancing digital earth modeling: Hexagonal multi-structural elements in icosahedral DGGS for enhanced geospatial data processing

Discrete Global Grid Systems (DGGSs) employ uniform cells for hierarchical Earth surface representation. However, prevalent software systems stemming from DGGS only focus on grid centers and lack the modeling of vertices and edges, which hindering efficient geospatial data computation and analysis. Consequently, we propose a novel mathematical model for hexagonal centers, vertices and edges of an icosahedral DGGS and provide corresponding hierarchical indexing methods and conversion rules with respect to integer coordinates. The proposed method was incorporated into the open-source software DGGRID, thereby enhancing its capabilities by integrating data fusion into the computation framework and expanding its application scope. The results show that the introduction of multi-structural elements could lead to higher-precision modeling of raster and vector data.

ODRA2D – TWO-DIMENSIONAL CASCADE FLOOD MODELLING FOR THE ODRA RIVER – DEVELOPMENT AND APPLICATIONS

Aim of the study: Development of new hydrodynamic numerical tools for simulations of the flow in the Odra River valley for the flood hazard assessment and management Material and methods: Use of the MIKE21 software for models building. Geospatial (ArcGis) topographical data processing and analysis for model inputs and outputs. Extensive use of remote sensing data (digital terrain model and orthophoto maps) combined with traditional data sources. Results and conclusions: A cascade of twenty five MIKE21 models was developed to cover a nearly 600 km long section of the Odra River. The models were used in different studies, including: the assessment of flood hazards with regards to various scenarios, the assessment of flooding due to ice jamming and breaks in embankments, the evaluation of the effectiveness of flood mitigating works within the complex Hydrosystem of the city of Wrocław, the verification of historic flood data and stage-discharge relations, and also the verification of design discharges.

Integrated geospatial approach for adaptive rainwater harvesting site selection under the impact of climate change

The impact of global climate change on water resources is a pressing concern, particularly in arid and semi-arid regions, where water shortages are becoming increasingly severe. Rainwater harvesting (RWH) offers a promising solution to address these challenges. However, the process of selecting suitable RWH sites is complex. This paper introduces a comprehensive methodology that leverages various technologies and data sources to identify suitable RWH locations in the northern region of Iraq, considering both historical and future scenarios. The study employs remote sensing and geographic information systems to collect and process geospatial data, which are essential for the site selection process. AHP is utilized as a decision-making tool to assess and rank potential RWH locations based on multiple criteria, helping to prioritize the most suitable sites. The WLC approach is used to combine and weigh various factors, enabling a systematic evaluation of site suitability. To account for the uncertainty associated with future climate conditions, a stochastic weather generator is employed to simulate historical and future precipitation data for period (1980–2022) and (2031–2100). This ensures that the assessment considers changing climate patterns. Historical precipitation values ranged from 270 to 490 mm, while future projections indicate a decrease, with values varying from 255 to 390 mm. This suggests a potential reduction in available water resources due to climate change. The runoff for historical rainfall values ranged from 190 mm (poor) to 490 mm (very good). In the future projections, runoff values vary from 180 mm (very poor) to 390 mm (good). This analysis highlights the potential impact of reduced precipitation on water availability. There is a strong correlation between rainfall and runoff, with values of 95% for historical data and 98.83% for future projections. This indicates that changes in precipitation directly affect water runoff. The study incorporates several criteria in the model, including soil texture, historical and future rainfall data, land use/cover, slope, and drainage density. These criteria were selected based on the nature of the study region and dataset availability. The suitability zones are classified into four categories for both historical potential and future projections of RWH zones: very high suitability, covering approximately 8.2%. High suitability, encompassing around 22.6%. Moderate suitability, constituting about 37.4%. Low suitability, accounting for 31.8% of the study region. For the potential zones of RWH in the future projection, the distribution is as follows: very high suitability, approximately 6.1%. High suitability, around 18.3%. Moderate suitability, roughly 31.2%. Low suitability, making up about 44.4% of the study region. The research's findings have significant implications for sustainable water resource management in the northern region of Iraq. As climate change exacerbates water scarcity, identifying suitable RWH locations becomes crucial for ensuring water availability. This methodology, incorporating advanced technology and data sources, provides a valuable tool for addressing these challenges and enhancing the future of water management to face of climate change. However, more investigations and studies need to be conducted in near future in the study region.

Exploiting geospatial data of connectivity and urban infrastructure for efficient positioning of emergency detection units in smart cities

The detection of critical situations through the adoption of multi-sensor Emergency Detection Units (EDUs) can significantly reduce the time between the initial stages of urban emergencies and the actual responses to relieve its negative effects, usually through the rescuing of endangered people, the attending to eventual victims, and the mitigating of its causes. However, although the benefits of such units are well known, their proper positioning in a city is challenging when considering a limited set of available units. In this sense, data-driven approaches can be leveraged to provide a better perception of the urban environments under consideration, allowing emergency management systems to be tailored to the specificities of a target city, thus improving the positioning of EDUs. This article proposes the processing of geospatial data of emergency-related urban infrastructure to support the computing of risk zones in a city, which is retrieved from the OpenStreetMap database together with the map of streets within a defined area. Since risk zones indirectly indicate the proportional number of detection units to be deployed, for each configuration setting of the EDUs, we propose an algorithm that computes the positions for such units only on streets, in a balanced way. Furthermore, considering that EDUs are expected to report detected emergencies through a wireless connection, we have also modelled the coverage area of existing networks in a city, which is also processed according to a suitable dataset. The proposed algorithm performs a fine-grained positioning of EDUs based on the number of active networks, flexibly favouring the EDUs' connectivity requirements such as reliability, throughput, latency, and transmission costs according to the actual demands of any urban emergency management system. Experimental results with real data demonstrated the applicability of the proposed mathematical model and the associated algorithm, reinforcing its practical application for the planning and construction of smart cities.

THE DISTRICT'S LIVABILITY OF SURAKARTA CITY

Livability is the ability of a place to provide a comfortable and healthy atmosphere for living, doing activities, or working, which is built through urban physical order, accessibility of city services, ease of mobility, an opportunity to participate, and protection of nature so that all residents have a good quality of life. In Indonesia, the city that has the best livability value is Surakarta. However, livability in this city does not seem available to all residents because the service facilities are not evenly distributed in some areas, and many people still live in slums. Therefore, this study was structured to measure the livability of the Surakarta City area using a quantitative deductive approach. The analysis technique used is geospatial data processing and scoring. As a result, the three observed areas have fairly good livability with different scores.

Distributed Architecture of Geospatial Data Collection and Processing System based on Web Design Patterns

This article discusses the issues of improving the reactivity and stability of web-based geographic information systems. It proposes a solution based on the integration of well-known web design patterns, which provides high internal and low external coupling of modules in the plugin architecture of the application. A prototype system was developed utilizing web design and programming patterns such as Model-View-Controller pattern and message-based communication between modules. The experiment was conducted on the dataset based on the results of registering the values of the Earths geomagnetic field parameters and its variations. The experiment demonstrates increased performance when using the suggested approach. The results show a reduction in server response time by 47 %.

Understanding Road Traffic Evolution in an Urbanizing Context through Statistical and Geospatial Analysis

Urbanization, an accelerated process, is inherently coupled with complex issues, including the evolution of road traffic systems. This diversity in urbanization and transport infrastructure largely hinges on economic status and geographic positioning across cities. Leveraging the capabilities of remote sensing and Geographic Information Systems (GIS) in processing geospatial big data, this paper evaluates the urbanization level (UL) and road traffic level (RTL) in 212 prefecture-level cities using statistical and geospatial grid data. We aim to dissect the impact of UL on RTL, thus highlighting the specific challenges and opportunities across regions and pinpointing optimal urban development models. Our findings demonstrate (1) rapid development in UL across all cities, juxtaposed with a surge and then stagnation in RTL; (2) a positive correlation between UL and RTL that grows over time but weakens in later stages; (3) differentiated development models in different city tiers and regions; and (4) region-specific development models and optimization policies aimed at enhancing the symbiosis of urbanization and road traffic. This study underscores the pivotal role of the integration of statistical and geospatial data in understanding the dynamic intersection of urbanization and road traffic systems.

GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling

Sea level rise (SLR) poses a significant threat to shorelines and the environment in terms of flooding densely populated areas and associated coastal ecosystems. Scenario analysis is often used to investigate potential SLR consequences, which can help stakeholders make informed decisions on climate change mitigation policies or guidelines. However, SLR scenario analysis requires considerable geospatial data analytics and repetitive execution of SLR models for alternative scenarios. Having to run SLR models many times for scenario analysis studies leads to heavy computational needs as well as a large investment of time and effort. This study explores the benefits of incorporating cyberinfrastructure technologies, represented by scientific workflows and high-performance computing, into spatially explicit SLR modeling. We propose a scientific workflow-driven approach to modeling the potential loss of marshland in response to different SLR scenarios. Our study area is the central South Carolina coastal region, USA. The scientific workflow approach allows for automating the geospatial data processing for SLR modeling, while repetitive modeling and data analytics are accelerated by leveraging high-performance and parallel computing. With support from automation and acceleration, this scientific workflow-driven approach allows us to conduct computationally intensive scenario analysis experiments to evaluate the impact of SLR on alternative land cover types including marshes and tidal flats as well as their spatial characteristics.

Google Earth Engine: A Global Analysis and Future Trends

The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to the academic and research world. This article proposes a bibliometric analysis of the GEE platform to analyze its scientific production. The methodology consists of four phases. The first phase corresponds to selecting “search” criteria, followed by the second phase focused on collecting data during the 2011 and 2022 periods using Elsevier’s Scopus database. Software and bibliometrics allowed to review the published articles during the third phase. Finally, the results were analyzed and interpreted in the last phase. The research found 2800 documents that received contributions from 125 countries, with China and the USA leading as the countries with higher contributions supporting an increment in the use of GEE for the visualization and processing of geospatial data. The intellectual structure study and knowledge mapping showed that topics of interest included satellites, sensors, remote sensing, machine learning, land use and land cover. The co-citations analysis revealed the connection between the researchers who used the GEE platform in their research papers. GEE has proven to be an emergent web platform with the potential to manage big satellite data easily. Furthermore, GEE is considered a multidisciplinary tool with multiple applications in various areas of knowledge. This research adds to the current knowledge about the Google Earth Engine platform, analyzing its cognitive structure related to the research in the Scopus database. In addition, this study presents inferences and suggestions to develop future works with this methodology.

A MODEL OF COLLABORATIVE THEMATIC DEVELOPMENT OF EDUCATIONAL PROGRAMS IN THE FIELD OF GEOMATICS

In the last half-century, the knowledge and understanding of the problems generated by the climate variations have been facilitated by a spectacular evolution of monitoring techniques based on evolved means of acquisition, processing and interpretation of geospatial data. In parallel, but at a slower pace, the training methods specific to geomatics were given due importance in education. Faced with the real needs to process geospatial data applied to agriculture and the environment and the opportunities of creating many jobs in the sector of spatial environmental information, universities need teachers. But, in a large number of cases, they have been formed imperfectly and sometimes convey an approximate and questionable formation. Therefore, it is essential to establish a solid quality training based on an original approach in geomatics applied to agriculture, environment and sustainable development. The ERASMUS+ project “Capacity building in the field of geomatics applied to agriculture and environment in Tunisia (GEOMAG)” is an example of a modern approach to the way of effective training, developed in synergy with the rapid progress of the geospatial domain.

On the positioning of emergencies detection units based on geospatial data of urban response centres

Urban areas have been subject to emergency situations with different causes and sometimes dramatic consequences. With the advent of smart cities technologies, multi-emergency detection units could be conceived and implemented taking advantage of affordable sensing technologies and efficient decision algorithms, allowing quick and distributed detection of emergencies. However, a recurrent problem has been the positioning and further deployment of such detection units in a way that the particularities of each target city are properly considered. In this sense, this article proposes the processing of geospatial data about existing urban infrastructure associated with some critical response after an emergency is detected, selecting hospitals, police stations, fire departments, and metro stations, in the target city. These infrastructures are then exploited to define the novel concept of mitigation zones, which indirectly express the perceived level of urban resilience to emergencies. Then, four positioning algorithms are proposed to exploit the mitigation zones considering a defined set of available detection units for deployment. Since the proposed approach is valid for any city, provided that geospatial data is available, it could be largely adopted to support different smart city systems, potentially bringing significant results in this area.

Electronic Atlas of Climatic Changes in the Western Russian Arctic in 1950–2021 as Geoinformatic Support of Railway Development

The Arctic zone of the Russian Federation is one of the most intensively developing regions of the country. Amongst the major domains of economic and industrial growth and improvement is transport infrastructure and particularly the railway network. This area is being exposed to negative factors of rapid climate change that can significantly affect and compromise this activity. Thus, it is vital to take them into account during design, construction, and operation of the railway infrastructure facilities. This work details the production of a digital atlas comprising the 1950–2021 dynamics of the main hydrometeorological parameters: air and soil temperature, precipitation, wind speed, air and soil humidity, and snow cover thickness. The maps are based on climatic data derived from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis. In total there are 459, which are arranged into 7 chapters. The atlas geographically covers the western part of the Russian Arctic encompassing the regions of quite intensive transport development, which includes the construction of the Northern Latitudinal Railway. Original algorithms of geospatial data processing and their further representation as well as the maps compiled in GIS environment are discussed. Comprehensive analysis of climatic changes in the region of the Russian Arctic including detailed quantitative evaluation over 40 years is given. In the Discussion, we focus on those changes of the regional climate which, from our point of view, are the most significant for consideration by railway operators. The obtained results contribute to framing the theoretical basis of design, development, and sustainable operation of the railway infrastructure in the Arctic and facilitate the decision-making process. This is the first experience of building a specialized climatic cartographic product for the needs of the Russian railways, and to our knowledge the first atlas such as that in the world. In the future, the amassed experience may be transferred to other regions of the Russian Federation as well as similar regions in Canada, Sweden and Highland China that are also subject to significant climate change.

GDAL and PROJ Libraries Integrated with GRASS GIS for Terrain Modelling of the Georeferenced Raster Image

Libraries with pre-written codes optimize the workflow in cartography and reduce labour intensive data processing by iteratively applying scripts to implementing mapping tasks. Most existing Geographic Information System (GIS) approaches are based on traditional software with a graphical user’s interface which significantly limits their performance. Although plugins are proposed to improve the functionality of many GIS programs, they are usually ad hoc in finding specific mapping solutions, e.g., cartographic projections and data conversion. We address this limitation by applying the principled approach of Geospatial Data Abstraction Library (GDAL), library for conversions between cartographic projections (PROJ) and Geographic Resources Analysis Support System (GRASS) GIS for geospatial data processing and morphometric analysis. This research presents topographic analysis of the dataset using scripting methods which include several tools: (1) GDAL, a translator library for raster and vector geospatial data formats used for converting Earth Global Relief Model (ETOPO1) GeoTIFF in XY Cartesian coordinates into World Geodetic System 1984 (WGS84) by the ‘gdalwarp’ utility; (2) PROJ projection transformation library used for converting ETOPO1 WGS84 grid to cartographic projections (Cassini–Soldner equirectangular, Equal Area Cylindrical, Two-Point Equidistant Azimuthal, and Oblique Mercator); and (3) GRASS GIS by sequential use of the following modules: r.info, d.mon, d.rast, r.colors, d.rast.leg, d.legend, d.northarrow, d.grid, d.text, g.region, and r.contour. The depth frequency was analysed by the module ‘d.histogram’. The proposed approach provided a systematic way for morphometric measuring of topographic data and combine the advantages of the GDAL, PROJ, and GRASS GIS tools that include the informativeness, effectiveness, and representativeness in spatial data processing. The morphometric analysis included the computed slope, aspect, profile, and tangential curvature of the study area. The data analysis revealed the distribution pattern in topographic data: 24% of data with elevations below 400 m, 13% of data with depths −5000 to −6000 m, 4% of depths have values −3000 to −4000 m, the least frequent data (−6000 to 7000 m) <1%, 2% of depths have values −2000 to 3000 m in the basin, while other values are distributed proportionally. Further, by incorporating the generic coordinate transformation software library PROJ, the raster grid was transformed into various cartographic projections to demonstrate distortions in shape and area. Scripting techniques of GRASS GIS are demonstrated for applications in topographic modelling and raster data processing. The GRASS GIS shows the effectiveness for mapping and visualization, compatibility with libraries (GDAL, PROJ), technical flexibility in combining Graphical User Interface (GUI), and command-line data processing. The research contributes to the technical cartographic development.