The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This growth is fueled by several key factors. Firstly, the burgeoning adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced accessibility to a wider user base, including small and medium-sized enterprises (SMEs). Secondly, the escalating need for precise spatial data analysis in various applications, such as urban planning, geological exploration, and water resource management, is significantly boosting market demand. The increasing integration of GIS with other technologies like AI and IoT further amplifies its capabilities, leading to more sophisticated applications and increased market penetration. Finally, government initiatives promoting digitalization and smart city development across the globe are indirectly fueling this market expansion. However, certain restraints limit market growth. The high initial investment cost for advanced GIS software and the requirement for skilled professionals to operate these systems can be a barrier, especially for smaller organizations. Additionally, data security and privacy concerns related to the handling of sensitive geographical information pose challenges to wider adoption. Market segmentation reveals strong growth in the cloud-based GIS segment, driven by its inherent advantages, while applications in urban planning and geological exploration lead the application-based segmentation. North America and Europe currently hold significant market shares, with strong growth potential in the Asia-Pacific region due to increasing infrastructure development and government investments. Leading companies like Esri, Hexagon, and Autodesk are shaping the market landscape through continuous innovation and competitive pricing strategies, while the emergence of open-source options like QGIS and GRASS GIS provides alternative, cost-effective solutions.

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

This data package includes two related data files that can be used as input for habitat network analyses on amphibians using a specific habitat network analysis tool (HNAT; v0.1.2-alpha):

1. AmphibianHabitatNetwork_Parameters.xlsx
2. BiotopeMap_GothenburgRegion_withPondsRoadsAndBuildings.tif

HNAT is a plugin for the open-source Geographic Information System QGIS (https://qgis.org/en/site/). HNAT can be downloaded at https://github.com/SMoG-Chalmers/hnat/releases/tag/v0.1.2-alpha. To run the habitat network analyses based on the input data provided in this package one must install the plugin HNAT into QGIS. This software has been created by Chalmers within a research project financed by the Swedish government research council for sustainable development, Formas (FR -2021/0004), within the framework of the national research program "From research to implementation for a sustainable society 2021". The Excel-file contains the parameters for amphibians and the GeoTiff-file is representing a biotope raster map covering the Gothenburg region in western Sweden. SRID=3006 (Sweref99 TM). Pixel size =10x10 metres. The pixel values of the biotope map correspond to the biotope codes listed in the in the parameter file (see column “BiotopeCode”). For each biotope the parameter file holds biotope specific parameter values for two alternative amphibian models denoted “Amphibians_NMDWater_ponds” and Amphibians_NMDWater_ponds_NoFriction”. The two alternative parameter settings can be used to demonstrate the difference in model prediction with or without the assumption that amphibian movements are affected by barrier effects caused by roads, buildings and certain biotopes biotope types. The “NoFriction” version assumes that amphibian dispersal probability declines exponentially with increasing Euclidian distance whereas the other set assumes dispersal to be affected by barriers. Read the readme file for details on each parameter provided in the parameter file.

The GeoTiff-file is a biotope mape which has been created by combining a couple of publicly available geodata sets. As a base for the biotope map the Swedish land cover map NMD was used (https://geodata.naturvardsverket.se/nedladdning/marktacke/NMD2018/NMD2018_basskikt_ogeneraliserad_Sverige_v1_1.zip). To achieve a greater cartographic representation of small ponds, streams, buildings and transport infrastructure relevant for amphibian dispersal, reproduction and foraging, NMD was complemented by information from a number of vector layers. In total, 20 new biotope classes representing buildings of different height ranging from less than 5 m up to 100 m, were added to the basic land cover map. The heights were obtained by analyzing the LiDAR data provided by Swedish Land Survey (for details see Berghauser Pont et al., 2019). The data was rasterized and added on top of existing pixels representing buildings in the Swedish land cover map. The roads were separated into 101 new biotope classes with different expected number of vehicles per day. Instead of using statistics from the Swedish Transport Administration on observed number of vehicles per day relative traffic volumes were predicted based on angular betweenness centrality values calculated from the road network using PST (Place Syntax Tool, Stavroulaki et al. 2023). PST is an open-source plugin for QGIS (https://www.smog.chalmers.se/pst). Traffic volumes are expected to be correlated to the centrality values (Serra and Hillier, 2019). The vector layer with the centrality values was buffered by 15 m prior to rasterization. After that the new pixel values were added to the basic Land cover raster in sequence following the order of centrality values. Information on small streams with a maximum width of 6 m was added from a vector layer of Swedish streams (https://www.lantmateriet.se/en/geodata/geodata-products/product-list/topography-50-download-vector/). These lines where rasterized and added to the land cover raster by replacing the underlaying pixel values with new class specific pixel values. Small pondlike waterbodies was identified from the NMD data selecting contiguous fragments of the original NMD biotope class 61 with a smaller area than 1 hectare. Pixels representing the smaller water bodies was then changed to 201.

References Berghauser Pont M, Stavroulaki G, Bobkova E, et al. (2019). The spatial distribution and frequency of street, plot and building types across five European cities. Environment and Planning B: Urban analytics and city science 46(7): 1226-1242. Serra M and Hillier B (2019) Angular and Metric Distance in Road Network Analysis: A nationwide correlation study. Computers, Environment and Urban Systems 74: 194-207. Stavroulaki I, Berghauser Pont M, Fitger M, et al. (2023) PST Documentation_v.3.2.5_20231128, DOI:10.13140/RG.2.2.32984.67845.

Abstract

The dataset is a geodatabase focusing on the distribution of freshwater fish species in Northern Greece. The study area encompasses various lakes and rivers within the regions of Thrace, Eastern, Central, and Western Macedonia, and Epirus. It classifies fish species into three categories based on their conservation status according to the IUCN Red List: Critically Endangered, Endangered, and Vulnerable. The data analysis reveals that the study area is characterized by high fish diversity, particularly in certain ecosystems such as the Evros River, Strymonas River, Aliakmonas River, Axios River, Volvi Lake, Nestos River, and Prespa Lake. These ecosystems serve as important habitats for various fish species. Mapping of the dataset shows the geographic distribution of threatened fish species, indicating that Northern Greece is a hotspot for species facing extinction risks. Overall, the dataset provides valuable insights for researchers, policymakers, and conservationists in understanding the status of fish fauna in Northern Greece and developing strategies for the protection and preservation of these important ecosystems.

Methods

Data Collection: The dataset was collected through a combination of field surveys, literature reviews, and the compilation of existing data from various reliable sources. Here's an overview of how the dataset was collected and processed:

• Freshwater Fishes and Lampreys of Greece: An Annotated Checklist
• The Red Book of Endangered Animals of Greece
• The "Red List of Threatened Species"
• The study "Monitoring and Evaluation of the Conservation Status of Fish Fauna Species of Community Interest in Greece"
• The international online fish database FishBase

Data Digitization and Georeferencing: To create a comprehensive database, we digitized and georeferenced the collected data from various sources. This involved converting information from papers, reports, and surveys into digital formats and associating them with specific geographic coordinates. Georeferencing allowed us to map the distribution of fish species within the study area accurately.

Data Integration: The digitized and georeferenced data were then integrated into a unified geodatabase. The geodatabase is a central repository that contains both spatial and descriptive data, facilitating further analysis and interpretation of the dataset.

Data Analysis: We analyzed the collected data to assess the distribution of fish species in Northern Greece, evaluate their conservation status according to the IUCN Red List categories, and identify the threats they face in their respective ecosystems. The analysis involved spatial mapping to visualize the distribution patterns of threatened fish species.

Data Validation: To ensure the accuracy and reliability of the dataset, we cross-referenced the information from different sources and validated it against known facts about the species and their habitats. This process helped to eliminate any discrepancies or errors in the dataset.

Interpretation and Findings: Finally, we interpreted the analyzed data and derived key findings about the diversity and conservation status of freshwater fish species in Northern Greece. The results were presented in the research paper, along with maps and visualizations to communicate the spatial patterns effectively.

Overall, the dataset represents a comprehensive and well-processed collection of information about fish fauna in the study area. It combines both spatial and descriptive data, providing valuable insights for understanding the distribution and conservation needs of freshwater fish populations in Northern Greece.

Usage notes

The data included with the submission is stored in a geodatabase format, specifically an ESRI Geodatabase (.gdb). A geodatabase is a container that can hold various types of geospatial data, including feature classes, attribute tables, and raster datasets. It provides a structured and organized way to store and manage geographic information.

To open and work with the geodatabase, you will need GIS software that supports ESRI Geodatabase formats. The primary software for accessing and manipulating ESRI Geodatabases is ESRI ArcGIS, which is a proprietary GIS software suite. However, there are open-source alternatives available that can also work with Geodatabase files.

Open-source software such as QGIS has support for reading and interacting with Geodatabase files. By using QGIS, you can access the data stored in the geodatabase and perform various geospatial analyses and visualizations. QGIS is a powerful and widely used open-source Geographic Information System that provides similar functionality to ESRI ArcGIS.

For tabular data within the geodatabase, you can export the tables as CSV files and open them with software like Microsoft Excel or the open-source alternative, LibreOffice Calc, for further analysis and manipulation.

Overall, the data provided in the submission is in a geodatabase format, and you can use ESRI ArcGIS or open-source alternatives like QGIS to access and work with the geospatial data it contains.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value of approximately $45 billion by 2033. Key drivers include the rising adoption of cloud-based GIS solutions, enhanced data analytics capabilities, the proliferation of location-based services, and the growing need for precise spatial data analysis in various industries like urban planning, geological exploration, and water resource management. The market is segmented by application (Geological Exploration, Water Conservancy Projects, Urban Planning, Others) and type (Cloud-based, Web-based). Cloud-based solutions are gaining significant traction due to their scalability, accessibility, and cost-effectiveness. The increasing availability of high-resolution satellite imagery and advancements in artificial intelligence (AI) and machine learning (ML) are further fueling market expansion. While data security concerns and the high initial investment costs for some advanced solutions present restraints, the overall market outlook remains positive, with significant opportunities for both established players and emerging technology providers. Geographical expansion is another key aspect of market growth. North America and Europe currently hold a significant market share, owing to established GIS infrastructure and early adoption of advanced technologies. However, the Asia-Pacific region is expected to witness rapid growth in the coming years, driven by rising government investments in infrastructure development and increasing urbanization in countries like China and India. Competitive dynamics are shaping the market, with major players like Esri, Autodesk, Hexagon, and Mapbox competing on the basis of software features, data integration capabilities, and customer support. The emergence of open-source GIS solutions like QGIS and GRASS GIS is also challenging the dominance of proprietary software, offering cost-effective alternatives for various applications. The continued development and integration of advanced technologies like 3D mapping, real-time data visualization, and location intelligence will further enhance the capabilities of GIS mapping tools, driving market expansion and innovation across various sectors.

Aim: Biodiversity hotspots often span international borders, thus conservation efforts must as well. China is one of the most biodiverse countries and the length of its international land borders is the longest in the world; thus, there is a strong need for transboundary conservation. We identify China’s transboundary conservation hotspots and analyze the potential effects of the Belt and Road Initiative (BRI) on them to provide recommendations for conservation actions. Location: China, Asia Methods: We compiled a species list of terrestrial vertebrates that span China’s borders. Using their distribution, we extracted the top 30% of the area with the highest richness value weighted by Red List category and considered these transboundary hotspots for conservation priority. Then we analyzed protected area (PA) coverage and connectivity to identify conservation gaps. To measure the potential impact of the BRI, we counted the species whose distribution range is traversed by the BRI and cal..., Data summary: This is the dataset used in the Diversity and Distributions contribution article "Transboundary conservation hotspots in China and potential impacts of the Belt and Road Initiative". The dataset includes heat maps of the transboundary distribution of terrestrial vertebrates in China drawn by the authors, as well as selected hotspots in the top 30% by value. In addition, a rasterized 0-1 protected area layer for the study area is provided for research reproduction. The heatmap and hotspots of transboundary species distribution were created as follows: We compiled a list of transboundary terrestrial vertebrates in China from the International Union for Conservation of Nature (IUCN) Red List database (https://www.iucnredlist.org/). We downloaded data of all species of mammals, birds, amphibians and reptiles from the database and filtered those living in terrestrial ecosystems. We then filtered these species based on their geographic ranges, to retain species living both in Ch..., R 4.2.0, package include "sf","terra". Or alternative Qgis 3.22, ArcGIS 10.6.

In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German! Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.htmltechnische_details Link to the rope map website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann. Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.htmltechnische_details Link zur Seilaplan-Website: https://seilaplan.wsl.ch

Full details are in the download file "README_Dataset-SurvivalAntarcticBiota.md" Software and file formats used. All maps were created using the Antarctic GIS package 'Quantarctica' (https://www.qgis.org/en/site/about/case_studies/antarctica.html) in QGIS ver. 3.22.7. The ACBRs shown in figure 1 and Supplementary figures S1-S7 are included in an 'Environmental management' layer within Quantarctica and colours were chosen to match those used previously. For the land topography of Antarctica we used the shapefiles from 'Bedmachine' (downloaded from NSIDC, https://nsidc.org/data/nsidc-0756/versions/2) in QGIS ver. 3.22.7. Each input data file was saved as .csv files and imported individually into QGIS for: (1) all individual springtail occurrences (separated into each species), (2) geothermal sites (separated into large and small), (3) geochronological dated sites (separated into high refuge support, and low refuge support), and (4) eDNA signals of springtails. These data were then used to create figures 1 and 2 in the main manuscript, and for more detailed information in figures S1-S7 in Supplementary material. Compiled data accessibility. The .csv data files we used in QGIS for springtail records, geothermal and geochronological sites shown in figures 1 and 2 and figures S1-S7 are available at the Royal Society's figshare portal. We also include our QGIS file used to generate the supplementary figures (QGIS_suppl_figs.qgz) and the .qlr 'layer definition file' (All_layers_definition_QGIS.qlr) exported from QGIS, which can be imported into QGIS with Qantarctica along with Bedmachine, which maintains the symbols and colours we used in our figures.Funding provided by: Australian Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000923Award Number: SR200100005 Data collection. We focussed on ice-free terrain represented by 15 currently recognized Antarctic Conservation Biodiversity Regions (ACBRs); we do not include South Orkney Islands. We compiled all published occurrence records for all springtail species considered to be endemic or native from these 15 ACBRs and from our own unpublished records. We obtained the ten geothermal sites used in the analyses by Fraser et al. from their Table S6. We compiled the geochronological data from all known cosmogenic-nuclide data from Antarctica (https://www.ice-d.org/) and from publications that were used to scrutinise the datasets. Cosmogenic dating is uniquely suited to Antarctic environments, however, there are problematic samples and locations. We include a selection of cosmogenic datasets to represent sites that clearly (or potentially) delineate Last Glacial Maximum surface elevations, and reject datasets where results are inconclusive due to isotope inheritance or incomplete or inconclusive results. From the included datasets we divided cosmogenic sites into two categories based on the 100 km radius around each site (using the criteria from Fraser et al.): (1) those that showed unequivocal endemism; and (2) those where the provenance was equivocal. Setting these criteria, and using springtails as a proxy, was critical to identifying regions where glacial refuges for the vast majority of biota were most likely to have occurred. The origin of terrestrial biota in Antarctica has been debated since the discovery of springtails on the first historic voyages to the southern continent more than 120 years ago. A plausible explanation for the long-term persistence of life requiring ice-free land on continental Antarctica has, however, remained elusive. The default glacial eradication scenario has dominated because hypotheses to date have failed to provide a mechanism for their widespread survival on the continent, particularly through the Last Glacial Maximum when geological evidence demonstrates that the ice sheet was more extensive than present. Here, we provide support for the alternative nunatak refuge hypothesis – that ice-free terrain with sufficient relief above the ice sheet provided refuges and was a source for terrestrial biota found today. This hypothesis is supported here by an increased understanding from the combination of biological and geological evidence, and we outline a mechanism for these refuges during successive glacial maxima that also provides a source for coastal species. Our cross-disciplinary approach provides future directions to further test this hypothesis that will lead to new insights into the evolution of Antarctic landscapes and how they have shaped the biota through a changing climate.

We used ArcGIS 10.7.1. QGIS, R, or similar open-source software are alternatives.

Various data recorded by Historic England relating to aerial investigation and mapping projects. N.B. This is a dynamic dataset that is constantly evolving, not only with the addition of newly completed projects, but also with the reassessment of some earlier projects. See https://historicengland.org.uk/research/methods/airborne-remote-sensing/aerial-investigation/ for further details of Historic England's work with aerial sources.It's currently not possible to provide download access to the earlier hand drawn projects, which are only available as raster files, but these can be viewed via the Aerial Archaeology Mapping Explorer. We aim to create vector monument polygons for these features as the next phase of the project.More information and help with these the layers Detailed MappingThis layer shows the detailed mapping of archaeological features derived from aerial imagery; this includes photographic imagery from many decades taken specifically for archaeological purposes, as well as other photography taken for other reasons and airborne lidar. The data are symbolised initially based on their physical form i.e. cut/negative (e.g. pit, ditch etc) or built/positive (e.g. mound, bank etc) .

    Field name
    Field alias
    Description
    Mandatory Y/N


    LAYER
    LAYER
    The layer used for mapping
    Y


    PROJECT
    PROJECT
    Project name
    Y

    PERIOD
    PERIOD
    The presumed date/period assigned to the feature (terminology from FISH thesaurus)
    Y


    MONUMENT_TYPE
    MONUMENT_TYPE
    The presumed type/function assigned to the feature (terminology from FISH thesaurus)
    Y


    EVIDENCE_1
    EVIDENCE_1
    The primary evidence for the feature e.g. cropmark, earthwork etc (terminology from FISH thesaurus)
    Y


  SOURCE_1
    SOURCE_1
    The primary source for the feature e.g. aerial photo reference, documentary source etc
    Y


  EVIDENCE_2
    EVIDENCE_2
    Where available the latest evidence for the feature e.g. cropmark, earthwork etc (terminology from FISH thesaurus) N.B. This was the latest evidence seen and does not necessarily represent the current status of the feature.
    N


  SOURCE_2
    SOURCE_2
    Where available the latest source for the feature N.B. This was the latest evidence seen and does not necessarily represent the current status of the feature.
    N


    HE_UID
    HE_UID
    Composite of Unique identifier(s) used by Historic England
    Y

    HER_NO
    HER_NO
    Composite of Unique identifier(s) used by Historic Environment Records
    N

    DHEUID_1
    DHEUID_1
    Primary Unique identifier used by Historic England
    Y

    DHEUID_2
    DHEUID_2
    Secondary Unique identifier used by Historic England. Used where a feature may relate to more than one Historic England record
    N

    DHEUID_3 ~ 5
    DHEUID_3 ~ 5
    Additional Unique identifier used by Historic England. Used where a feature may relate to more than one Historic England record
    N


    HE_URL1
    HE_URL1
    URL link to the relevant Historic England record in Heritage Gateway
    Y

    HE_URL2
    HE_URL2
    URL link to the relevant Historic England record in Heritage Gateway
    N

    HE_URL3 ~ 5
    HE_URL3 ~ 5
    URL link to the relevant Historic England record in Heritage Gateway
    N


    DHERNO_1
    DHERNO_1
    Primary unique identifier used by the relevant Historic Environment Record (HER)
    Y


    DHERNO_2
    DHERNO_2
    Secondary unique identifier used by the relevant Historic Environment Record. Used where a feature may relate to more than one HER record
    N


    DHERNO_3 ~ 5
    DHERNO_3 ~ 5
    Tertiary unique identifier used by the relevant Historic Environment Record. Used where a feature may relate to more than one HER record
    N

    DHERPREF_1
    DHERPREF_1
    Primary alternative unique identifier used by the relevant Historic Environment Record. Some HERs use the same number for both the HER No. and the reference to link to the record; others use different numbers and give them different names e.g MonUID
    Y

    DHERPREF_2
    DHERPREF_2
    Secondary alternative unique identifier used by the relevant Historic Environment Record. Some HERs use the same number for both the HER No. and the reference to link to the record; others use different numbers and give them different names e.g MonUID Used where a feature may relate to more than one HER record
    N

    DHERPREF_3 ~ 5
    DHERPREF_3 ~ 5
    Additional alternative unique identifier used by the relevant Historic Environment Record. Some HERs use the same number for both the HER No. and the reference to link to the record; others use different numbers and give them different names e.g MonUID Used where a feature may relate to more than one HER record
    N


    HER_LINK_1
    HER_LINK_1
    URL link to the relevant Historic Environment Record (HER) record in Heritage Gateway
    Y


    HER_LINK_2
    HER_LINK_2
    URL link to the relevant Historic Environment Record (HER) record in Heritage Gateway
    N


    HER_LINK_3 ~ 5
    HER_LINK_3 ~ 5
    URL link to the relevant Historic Environment Record (HER) record in Heritage Gateway
    N

The data are symbolised initially based on their physical form i.e. cut/negative (e.g. pit, ditch etc) or built/positive (e.g. mound, bank etc)

    Layer name
    Colour (Hex)
    Description

Bank #A50026 Used to outline banks, platforms, mounds and spoil heaps.

Ditch #313695 Used to outline cut features such as ditches, ponds, pits or hollow ways.

    Extent of Feature
    #FDAE61 (Dashes)
    Used to depict the extent of large area features such as airfields, military camps, or major extraction.


    Ridge and Furrow Alignment
    #74ADD1
    Line or arrow(s) (hand drawn not a symbol) depicting the direction of the rigs in a block of ridge and furrow.


    Ridge and Furrow Area
    #74ADD1 (Dots)
    Used to outline a block of ridge and furrow .


    Slope
    #4575B4
     The top of the “T” indicates the top of slope and the body indicates the length and direction of the slope. Used to depict scarps, edges of platforms and other large earthworks.

    Structure
    #F46D43
     Used to outline structures including stone, concrete, metal and timber constructions e.g., buildings, Nissen huts, tents, radio masts, camouflaged airfields, wrecks, fish traps, etc.

You can find instructions on how to create a QGIS style file (.qml) to recreate our mapping symbology in QGIS via our Open Data Downloads page under Aerial Investigation Mapping data.Monument ExtentsThis layer shows the general extent of the monuments, created from multiple sources, primarily aerial imagery, but referring to other sources such as earthwork surveys, documentary evidence and any information available from the relevant Historic Environment Record etc. This differs from the 'Detailed Mapping' layer, which shows the individual features as they appear on the ground.

    Field name
    Field alias
    Description
    Mandatory Y/N


    LAYER
    LAYER
    The layer used for mapping
    Y

    HE_UID
    HE_UID
    Composite of Unique identifier(s) used by Historic England
    Y

    HER_NO
    HER_NO
    Composite of Unique identifier(s) used by Historic Environement Records
    N


    HE_UID1
    HE_UID1
    Primary Unique identifier used by Historic England
    Y

    HE_UID2
    HE_UID2
    Secondary Unique identifier used by Historic England. Used where a feature may relate to more than one Historic England record
    N

    HE_UID3 ~ 5
    HE-UID3 ~ 5
    Additional Unique identifier used by Historic England. Used where a feature may relate to more than one Historic England record
    N


    HE_URL1
    HE_URL1
    URL link to the relevant Historic England record in Heritage Gateway
    Y

    HE_URL2
    HE_URL2
    URL link to the relevant Historic England record in Heritage Gateway
    N

    HE_URL3 ~ 5
    HE_URL3 ~ 5
    URL link to the relevant Historic England record in Heritage Gateway
    N


    HERNO_1
    HERNO_1
    Primary unique identifier used by the relevant Historic Environment Record (HER)
    Y


    HERNO_2
    HERNO_2
    Secondary unique identifier used by the relevant Historic Environment Record. Used where a feature may relate to more than one HER record
    N


    HERNO_3 ~ 25
    HERNO_3 ~ 25
    Tertiary unique identifier used by the relevant Historic Environment Record. Used where a feature may relate to more than one HER record
    N

    HERPREF_1
    HERPREF_1
    Primary alternative unique identifier used by the relevant Historic Environment Record. Some HERs use the same number for both the HER No. and the reference to link to the record; others use different numbers and give them different names e.g MonUID
    Y

    HERPREF_2
    HERPREF_2
    Secondary alternative unique identifier used by the relevant Historic Environment Record. Some HERs use the same number for both the HER No. and the reference to link to the record; others use different numbers and give them different names e.g MonUID Used where a feature may relate to more than one HER record
    N

    HERPREF_3 ~ 25
    HERPREF_3 ~ 25
    Additional alternative unique identifier used by the relevant Historic Environment Record. Some HERs use the same number for both the HER No. and the reference to link to the record; others use different numbers and give them different names e.g MonUID Used where a feature may relate to more than one HER record
    N


    HER_LINK_1
    HER_LINK_1
    URL link to the relevant Historic Environment Record (HER) record in Heritage Gateway
    Y


    HER_LINK_2
    HER_LINK_2
    URL link to the relevant Historic Environment Record (HER) record in Heritage Gateway
    N


    HER_LINK_3 ~ 25
    HER_LINK_3 ~ 25
    URL link to the relevant Historic Environment Record (HER) record in Heritage Gateway
    N


    PROJECT
    project
    Project name
    Y

Project AreaThis layer shows the extent of the

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, a reasonable estimate based on industry reports and the stated study period (2019-2033) suggests a current market valuation (2025) in the range of $3-5 billion USD. This significant market size is fueled by several key factors. The agricultural sector relies heavily on remote sensing for precision farming, crop monitoring, and yield prediction, significantly contributing to market expansion. Similarly, the water conservancy and forest management sectors utilize satellite imagery and software for resource monitoring, disaster management, and sustainable practices. Government agencies and the public sector increasingly adopt these technologies for urban planning, environmental monitoring, and national security applications. The market's growth is further enhanced by advancements in open-source software, offering cost-effective alternatives and promoting wider adoption. Trends such as cloud-based solutions, improved data processing capabilities, and the integration of artificial intelligence are further accelerating market growth. However, the market faces certain constraints. High initial investment costs for software licenses and specialized hardware can act as a barrier for entry, particularly for smaller businesses and organizations in developing regions. Data security concerns and the need for skilled professionals to interpret the complex data generated also pose challenges. Despite these obstacles, the ongoing development of user-friendly interfaces, coupled with decreasing hardware costs and increasing availability of cloud-based services, is predicted to mitigate these restraints and sustain a healthy compound annual growth rate (CAGR) in the range of 8-12% throughout the forecast period (2025-2033). Segmentation by application (Agriculture, Water Conservancy, Forest Management, Public Sector, Others) and software type (Open Source, Non-Open Source) reveals distinct market dynamics, with the non-open source segment currently holding a larger share due to its advanced capabilities. This trend is expected to continue, though the open-source segment will show considerable growth driven by its affordability and accessibility.

_\— (see comment in the Admin section) _ _ ** ** Strategic Noise Map Data produced by Impédance engineering on behalf of the metropolis Aix-Marseille Provence ** ** On the map, the geographical shapes have been simplified in order to optimise the display of the layer. The entire layer, without simplification of geographical shapes, is available in SHAPE format in the Export/Alternative Export tab. The symbology associated with the noise map is available in the attachment in QGIS format Road noise according to the Lden indicator for type A maps on the Aix-Marseille Provence Metropolis. Results of acoustic modelling calculations. Type A map locates areas exposed to noise. The Lden indicator (for Level day-evening-night) represents the weighted average noise level during the day by giving a stronger weight to the noise produced in the evening (18-22h) (+ 5 dB(A)) and during the night (22h-6h) (+ 10 dB(A)) to take into account the increased sensitivity of individuals to noise pollution during these two periods.

Limited availability of P in soils to crops may be due to deficiency and/or severe P retention. Earlier studies that drew on large soil profile databases have indicated that it is not (yet) feasible to present meaningful values for “plant-available” soil P, obtained according to comparable analytical methods, that may be linked to soil geographical databases derived from 1:5 million scale FAO Digital Soil Map of the World, such as the 5 x 5 arc-minute version of the ISRIC-WISE database. Therefore, an alternative solution for studying possible crop responses to fertilizer-P applied to soils, at a broad scale, was sought. The approach described in this report considers the inherent capacity of soils to retain phosphorus (P retention), in various forms. Main controlling factors of P retention processes, at the broad scale under consideration, are considered to be pH, soil mineralogy, and clay content. First, derived values for these properties were used to rate the inferred capacity for P retention of the component soil units of each map unit (or grid cell) using four classes (i.e., Low, Moderate, High, and Very High). Subsequently, the overall soil phosphorus retention potential was assessed for each mapping unit, taking into account the P-ratings and relative proportion of each component soil unit. Each P retention class has been assigned to a likely fertilizer P recovery fraction, derived from the literature, thereby permitting spatially more detailed, integrated model-based studies of environmental sustainability and agricultural production at the global and continental level (< 1:5 million). Nonetheless, uncertainties remain high; the present analysis provides an approximation of world soil phosphorus retention potential. The files are provided in ArcGIS and QGIS format.

Orthofoto Österreich ist ein Orthofotodienst von geoland.at, der im Rahmen von basemap.at als Web Map Tile Service angeboten wird. Es handelt sich um einen vorgenerierten Kachel-Cache, in der Web Mercator Auxiliary Sphere und damit kompatibel zu den gängigen weltweiten Basiskarten wie beispielsweise jenen von OpenStreetMap, Google Maps und Bing Maps. Bitte beachten Sie die Nutzungsbedingungen/Namensnennung, siehe weiterführende Metadaten.

Our South Korea zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.