Cross-border study programmes in science, mathematics and computing 2023 - Number of study programmes per university - Source: UniGR, DFHI-ISFATES

The global GIS Receiver market is experiencing robust growth, driven by increasing adoption in diverse sectors like surveying, construction, and precision agriculture. The market, valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising demand for precise geospatial data is creating significant opportunities for GIS receiver manufacturers. Secondly, technological advancements, such as the integration of improved GNSS technologies and higher accuracy sensors, are enhancing the capabilities and appeal of GIS receivers. Furthermore, the increasing penetration of affordable and user-friendly GIS software solutions is broadening the market's addressable audience. The market segmentation reveals a healthy demand across various receiver types (all-in-one and stand-alone) and applications (survey and mapping being dominant). Competition is intense, with established players like Hexagon, Trimble, and Topcon facing challenges from emerging regional competitors. The market's future growth trajectory is significantly influenced by factors like government investments in infrastructure projects, the expansion of smart cities initiatives, and the growing adoption of precision agriculture techniques. While the market presents significant opportunities, certain restraints need to be considered. The high initial investment cost associated with procuring advanced GIS receivers can act as a barrier for entry, particularly for small and medium-sized enterprises (SMEs). Furthermore, the market's growth is susceptible to fluctuations in economic conditions and government spending patterns. Another challenge arises from the complexities involved in data processing and interpretation, requiring specialized skills and expertise. However, the ongoing development of more user-friendly software and training programs are expected to alleviate this concern. The geographical distribution of the market shows a relatively even spread, with North America and Europe maintaining a strong presence, followed by a rapidly expanding Asia-Pacific region. This report provides a detailed analysis of the global GIS receiver market, a sector projected to exceed $5 billion in revenue by 2028. We delve into market concentration, key trends, dominant regions, product insights, and future growth catalysts. This in-depth study is invaluable for businesses involved in surveying, mapping, construction, and other sectors leveraging GNSS technology.

Formation transfrontalière UniGR: Master in Border Studies (MA) - Source: UniGR

The European location analytics market, valued at €4.17 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 15.60% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, the increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Secondly, the rising demand for real-time data insights across diverse sectors, including BFSI (Banking, Financial Services, and Insurance), retail, and transportation and logistics, is significantly boosting market growth. Furthermore, advancements in technologies like AI and machine learning are enhancing the accuracy and sophistication of location analytics, leading to more effective business strategies and improved operational efficiency. The growth is particularly evident in segments like indoor location analytics, driven by the rising need for precise indoor navigation and asset tracking within large buildings and shopping malls. Germany, the United Kingdom, and France are currently the largest national markets, reflecting strong technological infrastructure and high business adoption rates. However, market growth is not without challenges. Data privacy concerns and regulations, such as GDPR, present a significant restraint, requiring businesses to implement robust data security measures. Furthermore, the initial investment costs associated with implementing location analytics solutions can be a barrier to entry for some smaller businesses. Despite these challenges, the long-term outlook remains positive. The increasing integration of location analytics with other emerging technologies, such as the Internet of Things (IoT) and big data analytics, will unlock further opportunities and drive market expansion throughout the forecast period. The competitive landscape is characterized by a mix of established players and innovative startups, creating a dynamic environment for innovation and market evolution. This competitive environment fosters the development of advanced features and affordable solutions, thereby accelerating the adoption of location analytics across a broader spectrum of industries and applications. European Location Analytics Industry Market Report: 2019-2033 This comprehensive report provides an in-depth analysis of the European location analytics industry, encompassing market size, growth trends, competitive landscape, and future outlook. With a focus on key segments and geographical regions, this report is an essential resource for industry professionals, investors, and strategic decision-makers. The study period spans from 2019 to 2033, with 2025 serving as the base and estimated year. The forecast period is 2025-2033, and the historical period covers 2019-2024. The report values are presented in million units. Recent developments include: August 2023 - Cisco, one of the leaders in enterprise networking and security, and Nutanix, Inc., a leader in hybrid multi-cloud computing, announced a global strategic partnership to accelerate hybrid multi-cloud deployments by offering the industry's most complete hyper-converged solution for IT modernization and business transformation., July 2023 - Esri partnered with Impact Observatory and released a global land-use/land-cover map of the world based on the most up-to-date 10-meter Sentinel-2 satellite data for every year from 2017. Following the latest 2022 data released earlier, the artificial intelligence (AI) model for classification has been improved, making the maps more temporally consistent.. Key drivers for this market are: Increasing Use of Spatial Data and Analytics in Various Industries, Growing Propensity of Consumers Toward Applications that Use Location Data. Potential restraints include: Data Privacy Issues and Growing Regulations. Notable trends are: Cloud Segment is One of the Factors Driving the Market.

The feature class MO_interventi_public_later_3 — represents public intervention — punctual elements — acquired from the map of public interventions on a scale of 1:25 000 The PTPAAV maps (Piano Territoriale Paesestico Ambientale di Area Vasta) are a series of thematic maps drawn up since 1989 and finalised and approved at the end of November 1991, divided into territorial areas for a total of 8 areas identified in the regional territory. The work was carried out by several groups of technicians, a coordination group which established by circulars the standards to be used for the drafting of plans ranging from the thickness of the graph tip to the type of retino and the nuances to be used, and 8 design groups one for each area, which have created the maps trying to standardise spatial information as much as possible. The paperwork of this work was delivered to us in 2008 by the Environmental Heritage Office of the Molise Region. The latter already had scans of some thematic cards related to some areas, the missing ones and in the case of scans not found suitable for georeference, have been scanned. The mapping basis used by the working groups for the creation of PTPAAV maps was the IGM on a scale of 1:25,000.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. In the case of tropical cyclones, annual frequency is calculated using the category one of the Saffir-Simpson scale. It corresponds to the largest wind buffer of each past event footprint.

Sources: The dataset includes an estimate of tropical cyclone frequency of Saffir-Simpson category 1. It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. Unit is expected average number of event per 100 years multiplied by 100. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Europe.

UniGR cross-border study DFHI-ISFATES: Management Sciences (M.A.) - Source: DFHI-ISFATES

This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.

The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

The code producing this data set is publicly available on GitLab.

Variables:

• id = unique point identifier
• easting = x coordinate
• northing = y coordinate
• country = ISO country code
• species = Latin species name
• genus = genus name
• scientific_name = long species name
• gbif_taxon_key = taxon key from GBIF
• gbif_genus_key = genus key from GBIF
• taxon_rank = species or genus
• year = year of observation
• accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)
• dataset_info = data set name (individual sub-data-set)
• citation = DOI citation of the individual data set
• license = distribution license
• location_accuracy = spatial accuracy of observation (meters)
• flag_location_issue = known location issues present
• flag_date_issue = known date issues present
• eoo = Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (Mauri et al., 2017) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)
• dbh = Diameter Breast Height (only recorded for observations from the EU-Forest data set (Mauri et al., 2017))
• lc1 = LUCAS land cover type 1 (only recorded for observations from LUCAS data)
• lc2 = LUCAS land cover type 2 (only recorded for observations from LUCAS data)
• landmask_country = land mask overlay 30 meters (NA = not on land)
• corine = CORINE 2018 land cover type (extracted from the 100 meter raster data set)
• nightlights = light pollution observed by VIIRS (proxy for remoteness / distance to human structures)
• canopy_height = canopy height derived from GEDI waveform LiDAR point data
• natura_2000 = Natura 2000 site code (if a point falls inside a protected area (GIS-layer) this variable contains the site identification code; all sites can be explored on an interactive map)
• freq_location = number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)
• geometry = point geometry in ETRS89 / LAEA Europe

See this detailed documentation for more insights into each variable.

If you would like to know more about the creation of this data set, see

1. the R-Markdown documenting the process (GitLab repository)
2. the talk at OpenGeoHub Summer School 2020 (Youtube)

Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).

The feature class represents a sheet of the Regional Technical Paper 1:5000. The data was received from the Molise Region in shapefile format.

This dataset contains the raw data from the Wasserstoffatlas / Hydrogen Map published on wasserstoffatlas.de as listed here:

Description	Dataset Name	License of Raw Data	Source/Additional Information
Inventory, complete Dataset	Bestand.geojson	CC-BY-4.0	Methodoloy Paper Hydrogen Map
Potential, NUTS 0	Potential_nuts0.geojson	CC-BY-4.0	Methodoloy Paper Hydrogen Map
Potential, NUTS 1	Potential_nuts1.geojson	CC-BY-4.0	Methodoloy Paper Hydrogen Map
Potential, NUTS 2	Potential_nuts2.geojson	CC-BY-4.0	Methodoloy Paper Hydrogen Map
Potential, NUTS 3	Potential_nuts3.geojson	CC-BY-4.0	Methodoloy Paper Hydrogen Map

The Hydrogen Map relies on multiple data sources, the sources and licences are as follows (other sources not listed here may be used as well):

Description	Raw Data	License of Raw Data	Source/Additional Information
Solar potentials	vRES Generation Potentials (Europe NUTS-3)	CC-BY-4.0	München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2020.
Electricity demand of Tertiary Sector	Load Curves of the Tertiary Sector – eXtremOS solidEU Scenario (Europe NUTS-3)	CC-BY-4.0	München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.
Electricity demand of Transport Sector	Load Curves of the Transport Sector – eXtremOS solidEU Scenario (Europe NUTS-3)	CC-BY-4.0	München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.
Electricity demand of Private Household Sector	Load Curves of the Private Household Sector – eXtremOS solidEU Scenario (Europe NUTS-3)	CC-BY-4.0	München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.
Electricity demand of Industry Sector	Load Curves of the Industry Sector – eXtremOS solidEU Scenario (Europe NUTS-3)	CC-BY-4.0	München: Forschungsstelle für Energiewirtschaft e. V. (FfE), 2021.
Hydrogen refuelling stations	European Hydrogen Refuelling Station Availability System (E-HRS-AS)	EUPL-1.2	© Clean Hydrogen JU, 2020
Atlite	Atlite	data: CC0-1.0 & docs:CC-BY-4.0	© 2016-2021 The Atlite Authors.
PyPSA	PyPSA	(MIT Licence	© 2015-2022 PyPSA Developers
Agora hydrogen demand	No regret hydrogen study	CC-BY-4.0	Agora Energiewende and AFRY Management Consulting (2021): No-regret hydrogen: Charting early steps for H₂ infrastructure in Europe.
Bioethanolanlagen	Bioethanolwerke	CC-BY-4.0	BDBe - Bundesverband der deutschen Bioethanolwirtschaft e.V.
Biomethanaufbereitung	Biomethanaufbereitungsanlagen	CC-BY-4.0	Deutsche Energie-Agentur - biogaspartner (dena, 2023)
PRTR Germany	Thru.de des Umweltbundesamtes	CC-BY-4.0	Thru.de des Umweltbundesamtes
Raumordnungsplan BSH	GeoSeaPortal des BSH	dl-de/by-2-0	Bundesamt für Seeschifffahrt und Hydrographie (BSH) Download Raumordnungsplan AWZ (Veröffentlichung: 01.09.2021, letzte Änderung: 23.11.2021)
Markstammdatenregister	Marktstammdatenregister	dl-de/by-2-0	© 2022 Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahnen; Pressestelle

Funded by BMBF Germany (FKZ.: 03EW0013A).

The MM_Stazioni_Termometrie feature class is a punctual feature class that represents the stations where the temperature measurements are taken.

The lake mapping service market is experiencing robust growth, driven by increasing demand for effective resource management, environmental monitoring, and ecological studies. A rising global population and the consequent pressure on water resources are key factors fueling this expansion. Furthermore, advancements in remote sensing technologies, such as aerial photography and satellite imagery, are providing higher-resolution data, leading to more accurate and detailed lake mapping. This enhanced accuracy enables better informed decision-making for various applications, including identifying pollution sources, assessing water quality, monitoring shoreline changes, and managing aquatic vegetation. The market is segmented by application (environmental monitoring, resource management, ecological studies, and others) and type of imagery (aerial photography, satellite imagery, and others). While precise market sizing data was not provided, a conservative estimate based on industry trends and comparable markets suggests a current market value of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of around 7% projected through 2033. This growth is expected across all regions, with North America and Europe currently holding significant market shares due to higher adoption rates and established regulatory frameworks for environmental monitoring. However, Asia-Pacific is poised for significant expansion in the coming years due to increasing government investments in infrastructure and water resource management. Potential restraints include the high initial investment costs associated with advanced mapping technologies and the need for skilled professionals to interpret and utilize the data effectively. The competitive landscape is characterized by a mix of established environmental consulting firms and specialized lake management companies. Key players are focusing on strategic partnerships and technological advancements to strengthen their market positions. The increasing availability of affordable, high-resolution imagery and user-friendly data analysis software is democratizing access to lake mapping services, making them more accessible to smaller organizations and government agencies with limited budgets. This trend is expected to accelerate market penetration and contribute to overall market growth. The future of lake mapping services is linked to the integration of advanced analytics, artificial intelligence, and machine learning for improved data interpretation, predictive modeling, and automated reporting. This will enhance the efficiency and effectiveness of lake management initiatives globally.

Formation transfrontalière UniGR: Theatre Studies and Interculturality (M.A). - Source: UniGR

GIS point feature class of Scottish marine protected area search feature species from survey undertaken by Cefas and JNCC in January 2013 at the three Fladen Grounds pMPAs. This was a Scottish Marine Protected Areas (SMPA) site identification survey. The main aims were to confirm the presence of the Priority Marine Features and MPA Search Features recommended for protection within the pMPAs and to gather groundtruth data to compare benthic assemblages between, and within/outside, the sites.

The feature class MO_inq_soul_later_4 represents the soil pollution — point-type — acquired from the geomorphological map at a scale of 1:25 000. The maps PTPAAV (Territorial Environmental Country Plan of Area Vasta) are a series of thematic maps drawn up since 1989 and finished and approved at the end of November 1991, are divided into territorial areas for a total of 8 areas identified on the regional territory. The work was carried out by several groups of technicians, a coordination group which established by circulars the standards to be used for the drafting of plans ranging from the thickness of the graph tip to the type of retino and the nuances to be used, and 8 design groups one for each area, which have created the maps trying to standardise spatial information as much as possible. The paperwork of this work was delivered to us in 2008 by the Environmental Heritage Office of the Molise Region. The latter already had scans of some thematic cards related to some areas, the missing ones and in the case of scans not found suitable for georeference, have been scanned. The mapping basis used by the working groups for the creation of PTPAAV maps was the IGM on a scale of 1:25,000.

Punctual feature class indicating the air quality monitoring points of the Molise Region.

The feature class MO_Idrogeologia_idrografia represents the hydrography of the Molise Region, the data was provided to us by the regional office of the Geological Service

The MM_Erosion feature class is a working layer for the DSS. It cannot be consulted directly.