The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

Spatial Mapping Software Market Outlook

As per our latest research, the global spatial mapping software market size in 2024 stands at USD 7.2 billion, with a robust compound annual growth rate (CAGR) of 13.7% projected through 2033. By the end of 2033, the market is forecasted to reach a valuation of USD 22.1 billion. This impressive growth trajectory is primarily driven by the increasing adoption of location-based services, the proliferation of smart city initiatives, and the rising demand for geospatial analytics across various industries. The market is experiencing significant momentum as organizations seek advanced solutions for spatial data visualization, real-time mapping, and efficient resource management, thereby fueling the expansion of spatial mapping software globally.

The rapid digital transformation across industries is a major growth factor for the spatial mapping software market. As businesses and governments increasingly rely on data-driven decision-making, the ability to visualize, analyze, and interpret spatial data has become essential. Urbanization and the expansion of smart cities are creating a surge in demand for mapping solutions that enable planners and administrators to optimize infrastructure, manage assets, and monitor environmental impact. Furthermore, the integration of spatial mapping software with emerging technologies such as artificial intelligence, Internet of Things (IoT), and 5G networks is enhancing the precision and real-time capabilities of these platforms. This convergence is paving the way for innovative applications in areas such as autonomous vehicles, disaster response, and precision agriculture, further propelling market growth.

Another significant driver for the spatial mapping software market is the growing need for efficient asset management and risk mitigation. Organizations across sectors such as utilities, transportation, and defense are leveraging spatial mapping software to monitor and manage critical assets, detect anomalies, and ensure operational continuity. The ability to overlay real-time data on geographic maps provides unparalleled situational awareness, enabling quick and informed decision-making. Additionally, advancements in cloud computing have democratized access to sophisticated mapping tools, allowing even small and medium enterprises to benefit from spatial analytics without substantial infrastructure investments. The trend towards remote work and distributed operations post-pandemic has also accelerated the adoption of cloud-based mapping solutions, making spatial mapping an integral part of modern enterprise workflows.

Environmental monitoring and disaster management represent pivotal growth avenues for the spatial mapping software market. Climate change, urban sprawl, and natural disasters necessitate advanced solutions for tracking environmental changes, predicting hazards, and coordinating emergency responses. Spatial mapping software is being utilized to model flood zones, monitor deforestation, and track pollution, providing governments and organizations with actionable insights for sustainable development and disaster resilience. The increasing frequency and intensity of natural disasters globally have heightened the importance of real-time geospatial intelligence, driving investments in mapping technologies. As environmental regulations become stricter and public awareness grows, the demand for spatial mapping solutions in environmental monitoring is expected to remain strong throughout the forecast period.

The integration of Spatial Mapping Processor technology is revolutionizing the capabilities of spatial mapping software. This advanced processor enhances the speed and accuracy of data processing, allowing for more detailed and real-time analysis of spatial data. By leveraging the power of spatial mapping processors, organizations can achieve higher precision in mapping applications, which is crucial for sectors such as autonomous vehicles and smart city planning. The processor's ability to handle complex algorithms efficiently is enabling new levels of innovation in geospatial analytics, providing users with deeper insights and improved decision-making capabilities. As the demand for high-performance mapping solutions grows, the role of spatial mapping processors in driving technological advancements cannot be overstated.

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The Navigation and Mapping Solutions market is experiencing robust growth, driven by the increasing adoption of location-based services (LBS) across various sectors. The market's expansion is fueled by several key factors, including the proliferation of smartphones equipped with advanced GPS technology, the rising demand for real-time traffic updates and navigation assistance, and the increasing integration of mapping solutions into automotive systems. Furthermore, the development of sophisticated mapping technologies, such as 3D mapping and augmented reality (AR) overlays, is enhancing user experience and driving market penetration. The expanding use of these solutions in logistics and transportation, coupled with the growth of e-commerce and the demand for efficient delivery services, contributes significantly to the market's upward trajectory. We estimate the market size in 2025 to be around $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 12% through 2033. Despite the promising outlook, market growth faces certain challenges. High initial investment costs associated with developing and maintaining advanced mapping systems may limit entry for smaller players. Data privacy concerns and regulatory restrictions regarding data collection and usage pose significant hurdles. The accuracy and reliability of mapping data remain critical factors affecting market adoption, particularly in remote or rapidly changing areas. Competition among established players like Google, TomTom, and Garmin is intense, demanding continuous innovation and strategic partnerships to maintain a competitive edge. Despite these restraints, the long-term prospects for the navigation and mapping solutions market remain positive, driven by ongoing technological advancements and expanding applications across diverse industries.

The Ministry of Natural Resources and Forestry’s Make a Topographic Map is a mapping application that features the best available topographic data and imagery for Ontario. You can: * easily toggle between traditional map backgrounds and high-resolution imagery * choose to overlay the topographic information with the imagery * turn satellite imagery on or off * customize your map by adding your own text * print your custom map Data features include: * roads * trails * lakes * rivers * wooded areas * wetlands * provincial parks * municipal, township and other administrative boundaries You don’t need special software or licenses to use this application. Technical information Using cached imagery and topographic data, the application provides a fast, seamless display at pre-defined scales. The caches are updated annually.

Connected Map Services Market Outlook

According to our latest research, the global connected map services market size reached USD 18.7 billion in 2024, exhibiting robust growth propelled by the increasing adoption of digital mapping technologies across various industries. The market is projected to grow at a CAGR of 13.2% from 2025 to 2033, with the total value anticipated to reach USD 54.6 billion by 2033. This surge is primarily driven by the integration of real-time data analytics, expanding use cases in navigation and asset tracking, and the proliferation of Internet of Things (IoT) devices that demand seamless geospatial intelligence.

The growth trajectory of the connected map services market is underpinned by several critical factors, chief among them being the rapid technological advancements in geospatial analytics and mapping software. The ongoing digital transformation across sectors such as automotive, logistics, and urban planning is fostering a heightened demand for real-time, accurate, and interactive mapping solutions. Organizations are increasingly leveraging connected map services to enhance operational efficiency, improve customer experiences, and support data-driven decision-making. The proliferation of smartphones and connected vehicles has further accelerated the integration of mapping services, enabling businesses and consumers alike to access location-based data instantaneously. This widespread adoption is also being facilitated by advancements in cloud computing, which allow for scalable and flexible deployment of mapping solutions without the need for extensive on-premises infrastructure.

Another significant driver fueling market expansion is the growing emphasis on smart mobility and intelligent transportation systems. Governments and private enterprises are investing heavily in digital infrastructure to support smart city initiatives, which rely extensively on connected map services for traffic management, route optimization, and emergency response coordination. The rise of autonomous vehicles and the increasing adoption of electric vehicles have also created new opportunities for advanced navigation and fleet management solutions. Moreover, the integration of artificial intelligence and machine learning algorithms into mapping platforms is enabling more sophisticated data analysis, predictive modeling, and personalized user experiences. These technological innovations are not only enhancing the accuracy and relevance of map services but are also opening up new revenue streams for service providers.

The expanding ecosystem of IoT devices and the surge in demand for location-based services are further catalyzing the growth of the connected map services market. Businesses across sectors such as retail, utilities, and public safety are utilizing connected maps to track assets, monitor supply chains, and optimize field operations. The ability to overlay real-time data from sensors, cameras, and other connected devices onto digital maps is providing organizations with unprecedented visibility into their operations. This, in turn, is driving investments in both hardware and software components of connected map services, as companies seek to enhance their competitive edge through improved spatial intelligence. The increasing availability of high-speed internet and the rollout of 5G networks are also contributing to the seamless delivery of rich, interactive mapping experiences, further accelerating market adoption.

From a regional perspective, North America currently leads the global connected map services market, accounting for the largest share of revenue in 2024. This dominance can be attributed to the presence of major technology players, robust digital infrastructure, and early adoption of advanced mapping solutions in sectors such as automotive and logistics. Europe and Asia Pacific are also witnessing significant growth, driven by government initiatives to develop smart transportation networks and the rapid expansion of e-commerce and urbanization. Emerging economies in Latin America and the Middle East & Africa are gradually catching up, with increasing investments in digital infrastructure and growing awareness of the benefits of connected map services. As these regions continue to embrace digital transformation, the global market is expected to witness sustained growth over the forecast period.

Component Analysis

The connected map services market is segmented by component into software, hard

Shore area wetlands (lacustrine fringe) play a critical role as ecotones that support biodiversity, provide habitats for spawning and refuge, and exhibit high levels of primary productivity. They facilitate significant exchanges of materials between aquatic and terrestrial ecosystems. To effectively manage and preserve these important resources, it is essential to understand their distribution, size, and dynamic changes. This study aimed to create an accurate map of shoreline wetlands using multi-temporal and multi-source data, wetland indicators such as wetland hydrology (WH), hydrophytic vegetation (HV), hydric soil (HS), and radar imagery from Sentinel-1A, employing Geomatica software. Additionally, ArcGIS software was used to map the topographic position (TP), Lake Bathymetry (LB), and HS indicators for wetlands. The analytical hierarchy process and weighted overlay methods were also applied in the mapping process for integrating all the indicators to obtain the final extent of shoreline wetlands. The TP wetland indicator map covered about 55,364 ha, while HS covered around 55,151 ha within a 3 km buffer from Lake Tana. The map of WH indicator for wetlands revealed that permanently inundated areas accounted for roughly 591,312 ha, and when temporarily inundated areas were included, the total coverage increased to 607,053 ha. HV, including invasive water hyacinth, covered over 74,772 ha. Overall, shoreline wetlands were predominantly located within three kilometers of the terrestrial area from Lake Tana, totaling 26,664 ha. The overall accuracy of land use and cover classification was recorded at 79%, with a Kappa statistic of 0.70, indicating that the resulting map is of acceptable quality. The integration of multi-temporal and multi-source data, along with wetland indicators and radar imagery from Sentinel-1A using Geomatica software, has provided valuable insights into the spatial distribution of shoreline wetlands in Lake Tana. The findings from this study will serve as an important reference for future research aimed at effectively managing and conserving these vital resources.

1. This dataset mainly describes the geographic information of the water quality and quantity protection zones for tap water, to be used by government agencies and private organizations, groups, or academic units commissioned by government agencies. The fields display the Chinese names of the tap water quality and quantity protection zones, and the geographic spatial data is represented as areas, with a total of 107 records.2. According to Article 11 of the Tap Water Act, the establishment of protection zones is to ensure the quality of water and its supply, and activities that may cause pollution or development are restricted or prohibited within the designated areas.3. When opening this file using Google Earth software, slight discrepancies may occur in layer overlay due to the lack of precise orthorectification in the base map images provided by Google.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS 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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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS 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.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology

Map Rendering Engine for Automotive Market Outlook

According to our latest research, the global Map Rendering Engine for Automotive market size reached USD 2.35 billion in 2024, reflecting robust demand from the automotive sector. The market is exhibiting significant momentum, with a recorded CAGR of 12.8% from 2025 to 2033. By the end of 2033, the Map Rendering Engine for Automotive market is forecasted to attain a value of USD 6.94 billion. This impressive growth is primarily driven by the increasing integration of advanced navigation, ADAS, and infotainment systems in modern vehicles, as well as the rapid adoption of autonomous driving technologies across both developed and emerging economies.

One of the primary growth factors for the Map Rendering Engine for Automotive market is the surging demand for real-time, high-definition mapping solutions in vehicles. As consumers increasingly expect seamless navigation and safety features, automotive manufacturers are prioritizing advanced map rendering technologies to deliver precise, real-time geographic data. This trend is further amplified by the proliferation of connected vehicles and the Internet of Things (IoT), which require sophisticated mapping engines capable of processing vast amounts of spatial data and delivering dynamic updates. The growing complexity of road networks, urbanization, and the need for accurate lane-level guidance are also compelling automakers to invest heavily in next-generation map rendering engines.

Another significant driver is the rapid evolution of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. These applications demand ultra-reliable, low-latency map rendering engines that can seamlessly integrate with sensors, cameras, and LIDAR systems to provide real-time situational awareness. As automotive OEMs race to meet regulatory safety standards and consumer expectations for driverless mobility, the importance of map rendering technology has become paramount. The integration of AI and machine learning algorithms into rendering engines is further enhancing their capability to interpret complex environments and adapt to changing road conditions, thereby supporting the broader adoption of autonomous driving solutions.

A third key growth catalyst is the expanding role of infotainment systems in modern vehicles. Today’s consumers expect immersive digital experiences, including 3D maps, augmented reality overlays, and personalized content, all of which require advanced map rendering capabilities. Automakers are responding by partnering with technology providers to embed sophisticated rendering engines that support multi-modal navigation, real-time traffic updates, and location-based services. Additionally, the shift towards electric and connected vehicles is opening new avenues for map-based features such as range prediction, charging station location, and route optimization, further propelling market growth.

From a regional perspective, Asia Pacific is emerging as the dominant market, driven by the rapid expansion of automotive manufacturing hubs in China, Japan, South Korea, and India. This region accounted for approximately 37% of the global Map Rendering Engine for Automotive market in 2024. North America and Europe also represent significant markets, fueled by strong demand for premium vehicles and advanced mobility solutions. Meanwhile, regions such as Latin America and the Middle East & Africa are witnessing steady growth, supported by infrastructure development and increasing vehicle penetration. The global competitive landscape is being shaped by both established technology giants and innovative startups, each vying to capture a share of this rapidly evolving market.

Component Analysis

The Component segment of the Map Rendering Engine for Automotive market is classified into software, hardware, and services. Software remains the largest and fastest-growing sub-segment, accounting for nearly 57% of the total market revenue in 2024. The surge in demand for sophisticated mapping algorithms, 3D visualization, and real-time rendering capabilities is driving automotive OEMs and technology providers to invest heavily in software innovation. These solutions enable seamless integration with vehicle systems, support dynamic updates, and ensure high precision in navigation and ADAS applications. The software segment’s growth is also propelled by the rise of electric and autonomous vehicles, which requi

This dataset mainly describes the locations of groundwater quality monitoring stations under the jurisdiction of the Water Resources Agency and its affiliated agencies, to provide government agencies and private organizations, groups, or academic units commissioned by government agencies with the existing station area names for use. The spatial data is represented in point form. If you open this file using Google Earth software, there may be some errors in layer overlay due to the lack of precise orthorectification of the base map images provided by Google.

The "EMODnet Digital Bathymetry (DTM) - 2016" is a multilayer bathymetric product for Europe’s sea basins covering:: • the Greater North Sea, including the Kattegat and stretches of water such as Fair Isle, Cromarty, Forth, Forties, Dover, Wight, and Portland • the English Channel and Celtic Seas • Western and Central Mediterranean Sea and Ionian Sea • Bay of Biscay, Iberian coast and North-East Atlantic • Adriatic Sea • Aegean - Levantine Sea (Eastern Mediterranean) • Azores - Madeira EEZ • Canary Islands • Baltic Sea • Black Sea • Norwegian – Icelandic seas

The DTM is based upon more than 7700 bathymetric survey data sets and Composite DTMs that have been gathered from 27 data providers from 18 European countries and involving 169 data originators. The gathered survey data sets can be discovered and requested for access through the Common Data Index (CDI) data discovery and access service that also contains additional European survey data sets for global waters. This discovery service makes use of SeaDataNet standards and services and have been integrated in the EMODnet portal (https://emodnet.ec.europa.eu/en/bathymetry#bathymetry-services ). The Composite DTMs are described using the Sextant Catalogue Service that makes also use of SeaDataNet standards and services. Their metadata can retrieved through interrogating the Source Reference map in the Central Map Viewing service (https://emodnet.ec.europa.eu/geoviewer/ ). In addition, the EMODnet Map Viewer gives users wide functionality for viewing and downloading the EMODnet digital bathymetry such as: • water depth (refering to the Lowest Astronomical Tide Datum - LAT) in gridded form on a DTM grid of 1/8 * 1/8 arc minute of longitude and latitude (ca 230 * 230 meters) • option to view depth parameters of individual DTM cells and references to source data • option to download DTM in 16 tiles in different formats: EMO, EMO (without GEBCO data), ESRI ASCII, ESRI ASCII Mean Sea Level, XYZ, NetCDF (CF), RGB GeoTiff and SD • layer with a number of high resolution DTMs for coastal regions • layer with wrecks from the UKHO Wrecks database. The NetCDF (CF) DTM files are fit for use in a special 3D Viewer software package which is based on the existing open source NASA World Wind JSK application. It has been developed in the frame of the EU FP7 Geo-Seas project (another sibling of SeaDataNet for marine geological and geophysical data) and is freely available. The 3D viewer also supports the ingestion of WMS overlay maps. The SD files can also be used for 3D viewing by means of the freely available iView4De(Fledermaus) software.

The original datasets themselves are not distributed but described in the metadata services, giving clear information about the background survey data used for the DTM, their access restrictions, originators and distributors and facilitating requests by users to originator.

1. This dataset mainly describes the existing station location map data of the rain gauge stations under the jurisdiction of the Water Resources Agency and its affiliated agencies. It is used to provide government agencies and private institutions, groups, or academic units commissioned by government agencies with the existing station names. The geographic spatial data is represented as points, and the total number of data entries is 215.2. In order to fully receive real-time hydrological information at any time, rain gauge stations and water level stations are set up in various river basins and waterways. The purpose is to conduct comprehensive hydrological and hydraulic observations throughout the week to obtain sufficient basic data.3. This data only includes basic station information and does not include actual observation results.4. When using the Google Earth software to open this file, some discrepancies may occur during the layer overlay due to the lack of precise orthorectification of the base map images provided by Google.

On the basis of a large number of field investigations and verifications, a classification system suitable for the division of desert ecosystems on the Qinghai-Tibet Plateau has been constructed, which includes 11 tertiary types: rocky desert ecosystem with vegetation, rocky desert without vegetation, gravelly desert ecosystem with vegetation, gravelly desert without vegetation, sandy desert ecosystem with vegetation, sandy desert without vegetation, loamy desert ecosystem with vegetation, loamy desert without vegetation, saline-alkali desert ecosystem with vegetation, saline-alkali desert without vegetation, and alpine frigid desert. This dataset mainly utilizes the Landsat remote sensing image data of four periods in 1990, 2000, 2010, and 2020 downloaded from the official website of the USGS (United States Geological Survey) and the Geospatial Data Cloud. Due to the influence of clouds, snow, etc., the data of some regions have been extended to the image data of 2-3 years before and after. The preprocessing work of all remote sensing image data has been completed through projection transformation, geometric correction, clipping, and mosaicking. Mainly using eCognition software and ArcGIS software, referring to the soil type map and the 1:1,000,000 vegetation type map, etc., through the method of combining machine learning and manual visual interpretation, the multi-period mapping research work of the distribution of desert ecosystem types with a spatial resolution of 30 meters in the Qinghai-Tibet Plateau region from 1990 to 2020 has been completed. In order to ensure data consistency and quality, through the method of overlay analysis of maps and images, the pseudo-change patches in the classification results of the four periods have been removed, and the small fragmented patches smaller than 5*5 pixels have been merged and deleted, forming this dataset. A large number of field investigation sample points and high-definition Google Earth image grid point information have been selected to verify the data, with a total accuracy of over 90%. This dataset can serve as important basic data for aspects such as the distribution range of desert ecosystems in the Qinghai-Tibet Plateau region, the assessment of the ecological effects of desert ecosystems, and ecosystem management.

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--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping

The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:

(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)

(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:

a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile

b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to https://imagej.nih.gov/ij/developer/macro/macros.html#tools on how to install and run ImageJ macros.

(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:

a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")

b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).

c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format):

I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL

II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98

d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.

These files are placed in public domain under Creative Commons license CC BY-ND 4.0