Map Drawing Services Market Outlook

The global map drawing services market size was valued at approximately $1.2 billion in 2023 and is projected to reach $2.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% during the forecast period. This growth can be attributed to the increasing demand for precise and customized mapping solutions across various industries such as urban planning, environmental management, and tourism.

One of the primary growth factors of the map drawing services market is the rapid advancement in Geographic Information Systems (GIS) technology. The integration of advanced GIS tools allows for the creation of highly accurate and detailed maps, which are essential for urban planning and environmental management. Additionally, the growing emphasis on smart city initiatives worldwide has led to an increased need for customized mapping solutions to manage urban development and infrastructure efficiently. These technological advancements are not only improving the quality of map drawing services but are also making them more accessible to a broader range of end-users.

Another significant growth factor is the rising awareness and adoption of map drawing services in the tourism sector. Customized maps are increasingly being used to enhance the tourist experience by providing detailed information about destinations, routes, and points of interest. This trend is particularly prominent in regions with rich cultural and historical heritage, where detailed thematic maps can offer tourists a more immersive and informative experience. Furthermore, the digitalization of the tourism industry has made it easier to integrate these maps into various applications, further driving the demand for map drawing services.

Environmental management is another key area driving the growth of the map drawing services market. With the increasing focus on sustainable development and environmental conservation, there is a growing need for accurate maps to monitor natural resources, track changes in land use, and plan conservation efforts. Map drawing services provide essential tools for environmental scientists and policymakers to analyze and visualize data, aiding in better decision-making and management of natural resources. The rising environmental concerns globally are expected to continue driving the demand for these services.

From a regional perspective, North America is anticipated to hold a significant share of the map drawing services market due to the high adoption rate of advanced mapping technologies and the presence of major market players in the region. Furthermore, the region's focus on smart city projects and environmental conservation initiatives is expected to fuel the demand for map drawing services. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate, driven by rapid urbanization, industrialization, and the growing need for efficient infrastructure planning and management.

Service Type Analysis

The map drawing services market is segmented into several service types, including custom map drawing, thematic map drawing, topographic map drawing, and others. Custom map drawing services cater to specific client needs, offering tailored mapping solutions for various applications. This segment is expected to witness significant growth due to the increasing demand for personalized maps in sectors such as urban planning, tourism, and corporate services. Businesses and government agencies are increasingly relying on custom maps to support their operations, leading to the expansion of this segment.

Thematic map drawing services focus on creating maps that highlight specific themes or topics, such as population density, climate patterns, or economic activities. These maps are particularly useful for educational purposes, research, and community planning. The growing emphasis on data-driven decision-making and the need for visual representation of complex datasets are driving the demand for thematic maps. Additionally, thematic maps play a crucial role in public health, disaster management, and policy formulation, contributing to the segment's growth.

Topographic map drawing services offer detailed representations of physical features of a landscape, including elevation, terrain, and landforms. These maps are essential for various applications, such as environmental management, military ope

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

Digital Map Market is Segmented by Solution (Software, Services), Deployment (On-Premise, Cloud), Map Type (Navigation Maps, HD and Real-Time Maps, Topographic and Thematic Maps), End User Industry (Automotive, Engineering and Construction, Telecommunications and More), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

This application was created to support the Mapping Existing Vegetation on Prince of Wales Island story map.

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2^nd order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source.

The Drone GIS Mapping market is experiencing robust growth, driven by increasing demand for high-resolution geospatial data across various sectors. The market's expansion is fueled by advancements in drone technology, offering enhanced capabilities in image capture, processing, and analysis. Applications like precision agriculture, infrastructure monitoring (construction and energy), and mining operations are significantly contributing to market expansion. The thematic mapping segment holds a substantial market share due to its wide applicability in environmental monitoring, urban planning, and disaster management. Topographic mapping is also witnessing strong growth, driven by the need for accurate elevation data in construction and infrastructure projects. While the initial investment in drones and software can be a barrier to entry for some, the overall cost-effectiveness compared to traditional surveying methods, coupled with faster turnaround times, makes drone GIS mapping increasingly attractive. North America and Europe currently dominate the market, due to higher technological adoption and established GIS infrastructure; however, rapidly developing economies in Asia-Pacific are expected to demonstrate significant growth in the coming years, particularly in regions like China and India. The market is segmented by application (energy, construction, agriculture, mining, other) and by type of mapping (thematic, topographic, cadastral, navigation, series). This segmentation highlights the diverse applications of drone GIS mapping and drives further market diversification. Competition is currently strong, with various players offering a range of services and solutions. The forecast period of 2025-2033 presents promising opportunities for market expansion. Continued technological advancements, including improved sensor technology, AI-powered data processing, and cloud-based solutions, will further enhance the efficiency and accuracy of drone GIS mapping. Government initiatives promoting digitalization and infrastructure development will also play a crucial role in driving market growth. However, challenges such as regulatory hurdles regarding drone operations and data privacy, along with the need for skilled professionals to operate and interpret the data, will need to be addressed to ensure sustained market growth. The market is poised for significant expansion, with increasing adoption across diverse sectors and regions propelling its trajectory over the forecast period. We project a strong and sustained CAGR, reflecting the combined effect of technological advancement and growing market demands.

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2^nd order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source.

An attempt to collect, format, analyse and disseminate surveyed marine biological data deriving from the Eastern Mediterranean and Black Sea region is currently under development at the Hellenic Center for Marine Research (HCMR, Greece). The effort has been supported by the MedOBIS project (Mediterranean Ocean Biogeographic Information System) and has been carried out in cooperation with the Aristotelian University of Thessaloniki (Greece), the National Institute of Oceanography (Israel) and the Institute of Biology of the Southern Seas (Ukraine).

    The aim is to develop a taxon-based biogeography database and online data server with a link to survey and provide satellite environmental data. Currently, the primary features of the MedOBIS application are its offline GIS data formatting capabilities and its online Java and JavaScript enabling data server with taxon-based search, mapping and data downloading capabilities. In its completion, the MedOBIS online marine biological data system (http://www.iobis.org/OBISWEB/ObisDynPage1.jsp?content=meta/42.html) will be a single source of biological and environmental data (raw and analysed) as well as an online GIS tool for access of historical and current data by marine researchers. It will function as the Eastern Mediterranean and Black Sea node of EurOBIS (the European node of the International OBIS initiative, part of the Census of Marine Life).

    INTRODUCTION

    The international and interdisciplinary nature of the biological degradation issue as well as the technological advances of the Internet capabilities allowed the development of a considerable number of interrelated online databases. The free dissemination of valuable historical and current biological, environmental and genetic information has contributed to the establishment of an interdisciplinary platform targeted towards information integration at regional and also at global scales and to the development of information-based management schemes about our common interest.

    The spatial component of these data has led to the integration of the information by means of the Geographic Information System (GIS) technology. The latter is widely used as the natural framework for spatial data handling (Wright & Bartlett 1999, Valavanis 2002). GIS serves as the basic technological infrastructure for several online marine biodiversity databases available on the Internet today. Developments like OBIS (Ocean Biogeographic Information System, "http://www.iobis.org/"), OBIS-SEAMAP (Spatial Ecological Analysis of Megavertebrate Populations, "http://seamap.env.duke.edu/") and FIGIS (FAO Fisheries Global Information System, http://www.fao.org/fishery/figis) facilitate the study of anthropogenic impacts on threatened species, enhance our ability to test biogeographic and biodiversity models, support modelling efforts to predict distribution changes in response to environmental change and develop a strong potential for the public outreach component. In addition, such online database systems provide a broader view of marine biodiversity problems and allow the development of management practices that are based on synthetic analysis of interdisciplinary data (Schalk 1998, Decker & O'Dor 2002, Tsontos & Kiefer 2002).

    Towards this end, a development of a new online marine biological information system is presented here in its initial phase. MedOBIS (Mediterranean Ocean Biogeographic Information System) intends to assemble, formulate and disseminate marine biological data for the Eastern Mediterranean and Black Sea regions focusing on the assurance and longevity of historical surveyed data, the assembly of current and new information and the dissemination of raw and integrated biological and environmental data and future products through the Internet.

    MedOBIS DESCRIPTION

    MedOBIS current development consists of four main phases (Fig. 1). The data assembly phase is based on the free contribution of biological data from various national and international scientific surveys in the region. The data formatting phase is based on a GIS (ESRI, 1994), under which the geographic location of data stations is used to convert station data and their attributes to GIS shapefiles. The data analysis phase is based on data integration through GIS and spatial analyses (e.g. species distribution maps, species-environment relations, etc). Finally, the dissemination phase is based on ALOV Map, a free portable Java application for publication of vector and raster maps to the Internet and interactive viewing on web browsers. It supports navigation and search capabilities and allows working with multiple layers, thematic maps, hyperlinked features and attributed data.

    During the on-going data assembly phase, a total number of 776 stations with surveyed benthic biological data was employed. These data include mainly benthic species abundance (for nearly 3000 benthic organisms), benthic substrate types and several environmental parameters. Currently, 100 stations have been assembled for the Ionian Sea, 570 stations for the Aegean Sea and 106 stations for the Black Sea. The temporal resolution of these data extends for the period 1937-2000 while most data cover the period 1986-1996. Additionally, monthly satellite images of sea surface temperature (SST) and chlorophyll (Chl-a) were assembled for the period 1998-2003. Satellite data were obtained from the Advanced Very High Resolution Radiometer (AVHRR SST) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS Chl-a). 

    During the data formatting phase, all assembled surveyed stations were converted to a GIS shapefile (Fig. 2). This GIS information layer includes the geographic coordinates of the stations as well as stations' identification number. Station data attributes were organised in an MS Access Database while satellite data were embedded in a GIS database as GIS regular grids. The MedOBIS data analysis phase is still at the initial stage. Several off line analytical published efforts (e.g. Arvanitidis et al. 2002, Valavanis et al. 2004a,b,c) will be included in the MedOBIS development, which mainly focus on species distribution maps, mapping of productive oceanic processes and species-environment interactions. 

    The MedOBIS dissemination phase ("http://www.medobis.org/") is based on ALOV Map ("http://www.alov.org/"), a joint project of ALOV Software and the Archaeological Computing Laboratory, University of Sydney, Australia. ALOV Map is a Java-based application for publication of GIS data on the Internet and interactive viewing on web browsers. ALOV Map is designed to display geographical information stored in shapefiles or in any SQL database or even in an XML (Extensible Markup Language) document serving as a database. MedOBIS uses ALOV Map's full capabilities and runs in a client-server mode (Fig. 3). ALOV Map is connected to an MS Access database via a servlet container. This architecture was needed to connect the biological data with the spatial data and facilitate search options, such as, which species are found at which stations. Additionally, a JavaScript code is invoked, which searches the data, pops up a window with the results and then shows the relevant stations on the map.

    To provide a taxon-based search capability to the MedOBIS development, the sampling data as well as the relevant spatial data are stored in the database, so taxonomic data can be linked with the geographical data by SQL (Structured Query Language) queries. To reference each species to its location on the map, the database queries are stored and added to the applet as individual layers. A search function written in JavaScript searches the attribute data of that layer, displays the results in a separate window and marks the matching stations on the map (Fig. 4). Finally, selecting several stations by drawing a zooming rectangle on the map provides a list with predefined themes from which the user may select more information (Fig. 5). 

    CURRENT LIMITATIONS AND FUTURE PLANS

    A disadvantage of embedding information from the database as a layer is the relatively long download time due to the current MedOBIS-ALOV Map client-server architecture. An appropriate solution would be a direct search on the server side, which will allow partial data downloading to the client side. This work will be embedded in the MedOBIS application in the future (client-side architecture), when the size of assembled data becomes relatively 'heavy' for the current client-server architecture. This is an on-going process, since the MedOBIS initiative has been endorsed by the "Excellence of the Institute of Marine Biology of Crete (IMBC) in Marine Biodiversity", a Hellenic National Project that has been evaluated and approved by European experts. As more data will be assembled in time-series databases, an additional future work will include the development of MedOBIS data analysis phase, which is planned to include GIS modelling/mapping of species-environment interactions.

    Size reference: 2953 species; 776 stations

    [Source: The information provided in the summary was extracted from the MarBEF Data System at "http://www.marbef.org/data/eurobisproviders.php"]

Paraph 1: The global Drone GIS Mapping market is projected to reach a valuation of USD 1.5 billion by 2033, expanding at a CAGR of 12.5% from 2025 to 2033. The growing adoption of drone technology for aerial mapping, surveys, and inspections in various industries drives this market expansion. These industries include energy, construction, agriculture, mining, and others. Paraph 2: Key trends shaping the market include the increasing availability of high-resolution drone cameras, the development of advanced data processing algorithms, and the integration of AI and cloud computing. Restraints include concerns over data security and privacy, regulations governing drone operations, and the high cost of drone equipment. The market is segmented by application (energy, construction, agriculture, mining, and others) and mapping type (thematic mapping, topographic mapping, cadastral mapping, navigation mapping, and series mapping). North America and Europe hold dominant regional shares, while Asia Pacific and the Middle East & Africa are emerging as promising markets.

This database includes spatial ranges: western section of Gangdise, eastern section of Gangdise, eastern section of Nangangdise, and middle section of Nangangdise. The data content is: ① 1:250000 geological dataset (geological bodies and structures); ② Bulk metal mineral dataset (super large, large, medium, small deposits and mineral occurrences); The main maps include: Geological and Mineral Map of the Copper Lead Zinc Mineral Concentration Area in the Western Section of Gangdise (1:250000), Geological and Mineral Map of the Eastern Section of Gangdise (1:250000), Geological and Mineral Map of the Eastern Section of Nangangdise (1:250000), and Geological and Mineral Map of the Central Section of Nangangdise (1:250000). The spatial database adopts ArcGIS platform, which can provide basic data support for regional mineralization law research, resource potential assessment, strategic prospect area delineation, and various thematic map compilation. The database format is a file database (. GDB), which includes engineering files (MXD) and raster images (JPG). Various common graphic formats (PDF, TIF, EPS, etc.) can also be generated as needed. The coordinate system for geological and mineral data is Beijing 1954 GK Zone 15, using Gaussian Kruger projection. Data source and processing method: Basic geological data mainly comes from geological maps of various geological survey departments within the domain (1:200000); ② The main sources of mineral data include the results of the National Mineral Resource Potential Evaluation Project (2012), relevant information and data from various geological survey departments in the region, and relevant papers and publications on mineral resources in the region. In addition, to meet various data modification and improvement requirements, a large amount of remote sensing data is used, including ETM+, OLI, ASTER, Worldview and other image data, as well as 90m, 30m, 12.5m DEM data, etc. Data quality description: In order to meet the needs of studying the mineralization laws, geological mineral maps, and mineralization prediction maps in the Qinghai Tibet Plateau region, editing, processing, and supplementing are carried out in terms of data spatial accuracy, logical consistency, and data completeness. Specifically, it includes: ① Vectorization, based on the aforementioned data, a large amount of vectorization work has been carried out to supplement the missing areas of digital data. At the same time, according to the degree of data update, the elements of the segmentation plane and line are merged and segmented. Vectorization is completed in accordance with the accuracy requirements of the scale in relevant Chinese regulations. ② Topology processing to eliminate topological errors such as overlapping surfaces and voids; ③ We have improved the structure of element attributes and supplemented the content of element attributes, focusing on the research of regional mineralization laws, geological mineral maps, and mineralization prediction maps. Based on relevant regulations in China, combined with specific information and data content, we have established corresponding data models, improved the attribute structure of geological bodies, structures, and mineral element classes, and completed the filling of corresponding attributes Based on the above data processing content, combined with research results and the latest understanding of the Qinghai Tibet Plateau, further modifications and improvements have been made to the relevant geological content in the region. Data application achievements and prospects: 1:250000 geological and mineral maps, as well as information exchange extraction, analysis, and thematic map compilation of lithology, geological age, and stratigraphic zoning.

Access API

Metadata Portal Metadata Information

Access API

Metadata Portal Metadata Information

Access API

Metadata Portal Metadata Information

There have been a number of requests for "State Roads" data. This data is currently available via the Data.NSW Spatial Collaboration Portal.

To access Road Segment Data please follow the instructions below;

1. From https://portal.spatial.nsw.gov.au/
2. Click the Browse Data tile
3. Click NSW Data themes tile
4. Click the Transport icon
5. Click the link NSW Transport Theme - Road Name Extent

https://opendata.transport.nsw.gov.au/sites/default/files/styles/panopoly_image_original/public/Image%201.png?itok=Zj8I2HTQ&c=57759871b0db5f3b792ed8c6dc3c4669" alt="homepage">

1. Click on Export Data (highlighted above)

https://opendata.transport.nsw.gov.au/sites/default/files/styles/panopoly_image_original/public/Image%202.png?itok=uy1Gkd3E&c=2f9dc44fee9f3e79d7b5348119a8827f" alt="Export Page">

1. Do you want to select which layers to export? You can select Yes and choose from the layers provided OR you can select No Thanks. I want to export data from all layers. This will download all data sets. In the example below RoadNameExtent is selected.

https://opendata.transport.nsw.gov.au/sites/default/files/styles/panopoly_image_original/public/Image%203.png?itok=DMQQSR4S" alt="Layers to Export Page">

1. Click Next >

2. Do you want to specify an extent? Select No or Yes, by drawing the extent on a map. If you select ‘No’ all the data will be extracted. If you wanted to specify an extent of data extraction, e.g. around Sydney in the screenshot below, use the square icon labelled ‘Draw a rectangle’. This will draw a square centred on the point where you clicked on the map. You can change the shape from the icon labelled ‘Reshape’ to get the required area of extraction.

https://opendata.transport.nsw.gov.au/sites/default/files/styles/panopoly_image_original/public/Image%204.png?itok=T28ur_3b" alt="Map Selection View">

1. Click on Next >

2. Select your preferred Export format. Please note the limitation of ESRI Shape files truncating attribute names to 10 characters.

3. Select your preferred Export datum: Please note the current preference of GDA2020, although that depends on your objective.

4. Select your preferred Export coordinate system: ‘Geographic’ will export the geometries in latitude/longitude. MGAxx coordinate systems will export the geometries in metres.

5. Type your email address

https://opendata.transport.nsw.gov.au/sites/default/files/styles/panopoly_image_original/public/Image%205.png?itok=KQ2XbSQL" alt="Attribute Selection">

1. Click Export

2. You should see the screen below

https://opendata.transport.nsw.gov.au/sites/default/files/styles/panopoly_image_original/public/Image%206.PNG?itok=4isSITci" alt="Confirmation Screen">

1. Check your inbox for an email from Customer Service with a link to download the data.

This database includes spatial ranges: the northwest edge of the West Kunlun Mountains (including the Karakoram Mountains), the central part of the West Kunlun Mountains, and the southeast edge of the West Kunlun Mountains. The data content is: ① 1:250000 geological dataset (geological bodies and structures); ② Bulk metal mineral dataset (super large, large, medium, small deposits and mineral occurrences); The main maps include: Geological and Mineral Resources Map of the Iron Manganese Lead Zinc Mineral Concentration Area in the northwest margin of the West Kunlun Mountains (1:250000), Geological and Mineral Resources Map of the Iron Manganese Lead Zinc Mineral Concentration Area in the middle of the West Kunlun Mountains (1:250000), and Geological and Mineral Resources Map of the Iron Manganese Lead Zinc Mineral Concentration Area in the southeast margin of the West Kunlun Mountains (1:250000). The spatial database adopts ArcGIS platform, which can provide basic data support for regional mineralization law research, resource potential assessment, strategic prospect area delineation, and various thematic map compilation. The database format is a file database (. GDB), which includes engineering files (MXD) and raster images (JPG). Various common graphic formats (PDF, TIF, EPS, etc.) can also be generated as needed. The coordinate system for geological and mineral data is Xian 1980 GK Zone 13, using Gaussian Kruger projection. Data source and processing method: Basic geological data mainly comes from geological maps of various geological survey departments within the domain (1:200000); ② The main sources of mineral data include the results of the National Mineral Resource Potential Evaluation Project (2012), relevant information and data from various geological survey departments in the region, and relevant papers and publications on mineral resources in the region. In addition, to meet various data modification and improvement requirements, a large amount of remote sensing data is used, including ETM+, OLI, ASTER, Worldview and other image data, as well as 90m, 30m, 12.5m DEM data, etc. Data quality description: In order to meet the needs of studying the mineralization laws, geological mineral maps, and mineralization prediction maps in the Qinghai Tibet Plateau region, editing, processing, and supplementing are carried out in terms of data spatial accuracy, logical consistency, and data completeness. Specifically, it includes: ① Vectorization, based on the aforementioned data, a large amount of vectorization work has been carried out to supplement the missing areas of digital data. At the same time, according to the degree of data update, the elements of the segmentation plane and line are merged and segmented. Vectorization is completed in accordance with the accuracy requirements of the scale in relevant Chinese regulations. ② Topology processing to eliminate topological errors such as overlapping surfaces and voids; ③ We have improved the structure of element attributes and supplemented the content of element attributes, focusing on the research of regional mineralization laws, geological mineral maps, and mineralization prediction maps. Based on relevant regulations in China, combined with specific information and data content, we have established corresponding data models, improved the attribute structure of geological bodies, structures, and mineral element classes, and completed the filling of corresponding attributes Based on the above data processing content, combined with research results and the latest understanding of the Qinghai Tibet Plateau, further modifications and improvements have been made to the relevant geological content in the region. Data application achievements and prospects: 1:250000 geological and mineral maps, as well as information exchange extraction, analysis, and thematic map compilation of lithology, geological age, and stratigraphic zoning.

This database includes the spatial scope: the eastern section of the southern margin of Qiangtang, the middle section of the southern margin of Qiangtang, and the western section of the southern margin of Qiangtang. The data content is: ① 1:250000 geological dataset (geological bodies and structures); ② Bulk metal mineral dataset (super large, large, medium, small deposits and mineral occurrences); The main maps include: Geological and Mineral Map of the Eastern Section of Qiangtang South Edge Copper Lead Zinc Mineral Concentration Area (1:250000), Geological and Mineral Map of the Central Section of Qiangtang South Edge Copper Lead Zinc Mineral Concentration Area (1:250000), and Geological and Mineral Map of the Western Section of Qiangtang South Edge Copper Lead Zinc Mineral Concentration Area (1:250000). The spatial database adopts ArcGIS platform, which can provide basic data support for regional mineralization law research, resource potential assessment, strategic prospect area delineation, and various thematic map compilation. The database format is a file database (. GDB), which includes engineering files (MXD) and raster images (JPG). Various common graphic formats (PDF, TIF, EPS, etc.) can also be generated as needed. The coordinate system for geological and mineral data is Beijing 1954 GK Zone 15, using Gaussian Kruger projection. Data source and processing method: Basic geological data mainly comes from geological maps of various geological survey departments within the domain (1:200000); ② The main sources of mineral data include the results of the National Mineral Resource Potential Evaluation Project (2012), relevant information and data from various geological survey departments in the region, and relevant papers and publications on mineral resources in the region. In addition, to meet various data modification and improvement requirements, a large amount of remote sensing data is used, including ETM+, OLI, ASTER, Worldview and other image data, as well as 90m, 30m, 12.5m DEM data, etc. Data quality description: In order to meet the needs of studying the mineralization laws, geological mineral maps, and mineralization prediction maps in the Qinghai Tibet Plateau region, editing, processing, and supplementing are carried out in terms of data spatial accuracy, logical consistency, and data completeness. Specifically, it includes: ① Vectorization, based on the aforementioned data, a large amount of vectorization work has been carried out to supplement the missing areas of digital data. At the same time, according to the degree of data update, the elements of the segmentation plane and line are merged and segmented. Vectorization is completed in accordance with the accuracy requirements of the scale in relevant Chinese regulations. ② Topology processing to eliminate topological errors such as overlapping surfaces and voids; ③ We have improved the structure of element attributes and supplemented the content of element attributes, focusing on the research of regional mineralization laws, geological mineral maps, and mineralization prediction maps. Based on relevant regulations in China, combined with specific information and data content, we have established corresponding data models, improved the attribute structure of geological bodies, structures, and mineral element classes, and completed the filling of corresponding attributes Based on the above data processing content, combined with research results and the latest understanding of the Qinghai Tibet Plateau, further modifications and improvements have been made to the relevant geological content in the region. Data application achievements and prospects: 1:250000 geological and mineral maps, as well as information exchange extraction, analysis, and thematic map compilation of lithology, geological age, and stratigraphic zoning.

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2^nd order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source.