Datasets- simple_land_cover1.tif - an example land cover dataset presented in Figures 1 and 2- simple_landform1.tif - an example landform dataset presented in Figures 1 and 2- landcover_europe.tif - a land cover dataset with nine categories for Europe - landcover_europe.qml - a QGIS color style for the landcover_europe.tif dataset- landform_europe.tif - a landform dataset with 17 categories for Europe - landform_europe.qml - a QGIS color style for the landform_europe.tif dataset- map1.gpkg - a map of LTs in Europe constructed using the INCOMA-based method- map1.qml - a QGIS color style for the map1.gpkg dataset- map2.gpkg - a map of LTs in Europe constructed using the COMA method to identify and delineate pattern types in each theme separately- map2.qml - a QGIS color style for the map2.gpkg dataset- map3.gpkg - a map of LTs in Europe constructed using the map overlay method- map3.qml - a QGIS color style for the map3.gpkg dataset

Thematic maps about social inequalities can engage audiences, add context to policy debates, and change attitudes toward the issues. The field of communication has long compared the relative persuasiveness of this kind of abstract data versus concrete examples about individuals. While studies have compared the effectiveness of presenting both types of information alongside each other, the line between them is sometimes blurred in data visualization, which can incorporate individuals’ stories in innovative ways. One context in which incorporating examples within thematic maps may help is when discussing the social determinants of health because the complex relationship between individual and community is central to how the determinants influence health, and communication on this can be challenging. In this study, we randomly presented the UK public (N = 389) with maps incorporating varying levels of “exemplification” for three different social determinants: public transport, air pollution, and youth service provision. We tested how this affected engagement, credibility, and perceptions about the issues. Between-group analysis found few significant differences and therefore limited persuasive power. However, within-subject analysis indicated that the maps with individual-centered stories may be more persuasive but only among those less confident in their ability to interpret data visualizations. Maps of social inequalities that incorporate stories about individuals may be more engaging and persuasive to audiences less confident with statistics.In data visualization experiments, researchers should consider analyzing both differences between treatment groups and differences within subjects in their responses to different stimuli. Maps of social inequalities that incorporate stories about individuals may be more engaging and persuasive to audiences less confident with statistics. In data visualization experiments, researchers should consider analyzing both differences between treatment groups and differences within subjects in their responses to different stimuli.

Multispectral remote sensing data acquired by Landsat 8 Operational Land Imager (OLI) sensor were analyzed using an automated technique to generate surficial mineralogy and vegetation maps of the conterminous western United States. Six spectral indices (e.g. band-ratios), highlighting distinct spectral absorptions, were developed to aid in the identification of mineral groups in exposed rocks, soils, mine waste rock, and mill tailings across the landscape. The data are centered on the Western U.S. and cover portions of Texas, Oklahoma, Kansas, the Canada-U.S. border, and the Mexico-U.S. border during the summers of 2013 – 2014. Methods used to process the images and algorithms used to infer mineralogical composition of surficial materials are detailed in Rockwell and others (2021) and were similar to those developed by Rockwell (2012; 2013). Final maps are provided as ERDAS IMAGINE (.img) thematic raster images and contain pixel values representing mineral and vegetation group classifications. Rockwell, B.W., 2012, Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3190, 35 p. pamphlet, 5 map sheets, scale 1:100,000, http://doi.org/10.13140/RG.2.1.2769.9365. Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3252, 25 p. pamphlet, 1 map sheet, scale 1:325,000, http://doi.org/10.13140/RG.2.1.2507.7925. Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation from Landsat 8 Operational Land Imager Data: San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, scale 1:325,000, 51 p. pamphlet, https://doi.org/10.3133/sim3466/.

For this dataset, we used 50k soybean samples collected from thematic maps produced by the Global Land Analysis & Discovery group (GLAD).

A Collection of Contextual data for USA

These are anonymized responses to a survey of 389 members of the UK public on their perceptions towards different maps about the social determinants of health. It was originally collected as part of a study described in the article 'Do personal narratives make thematic maps more persuasive? Integrating concrete examples into maps of the social determinants of health', in the Cartography and Geographic Information Science journal.The responses were collected in September 2024 on Qualtrics, via the recruitment platform Prolific.Participants were shown information on three social determinants of health (public transport, air pollution, youth services). For each topic, they were randomly shown one of three maps with varying levels of personal narratives presented. The type of map shown to each respondent can be found in columns 'transport_condition', 'pollution_condition', and 'youth_condition'. Most of the other variables refer to perceptions about those issues. For example, 'severity_pollution' refers to whether they deem air pollution a severe issue facing the country. Other variables include demographic information, chart literacy measured by four questions, and self-assessed confidence with charts.

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent …Show full descriptionThis map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW. Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product. Soil data confidence is described using a 4 class system between high and very low as outlined below.: Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps. Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only. Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

Snapshot img

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 applications,

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: (i) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. (ii) The inclusion probabilities must be: (a) knowable for nonsampled units and (b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne Very High Resolution (VHR) images, where: (I) an original Categorical Variable Pair Similarity Index (CVPSI, proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and (II) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic MapperTM (SIAMTM) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAMTM by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAMTM pre-classification maps proposed in this contribution, together with OQIs claimed for SIAMTM by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAMTM software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems (GEOSS) initiative and the QA4EO international guidelines.

This layer is a georeferenced image of a map of the Ottoman Empire originally produced in 1893. "This is a key milestone in the history of Ottoman cartography, being the very first distance-time-route map of the entire Ottoman Empire. Published in Istanbul in 1893, it was produced by the General Staff (Fourth Division) of the Ottoman Army, predicated upon exhaustive highway surveys and itinerary records compiled over recent years. The map captures the scene during the middle of the rule of Sultan Abdul Hamid II (reigned 1876-1909), during which the empire still controlled vast territories in Europe, Asia and Africa, extending from Albania to Yemen and from Libya to the Persian Gulf. The Hamidian Era also marked a period of rapid modernization of the empire, including the creation of macadamized roads (highways), railways and modern ports. It also hailed the rise of highly sophisticated scientific and thematic cartography of the all regions of the realm created by Ottoman subjects, as opposed to Westerners. The main part of the map encompasses a great area, centred upon Anatolia, but taking in all the core regions of the Ottoman Empire, with its coverage extending from Bosnia, in the northwest, all the way down to Kuwait City and the head of the Persian Gulf, in southwest, and from Crimea and Baku, in the north and east, down to include Lower Egypt in the southwest. The scope is extended by insets that depict the extremities of the empire; in the lower right corner is an inset capturing the western Persian Gulf, including Kuwait, Bahrain, and Qatar; the inset above details the Red Sea, including Hejaz, Asir and Yemen; while the large inset in the lower felt depicts Ottoman Libya, as well as parts of French Tunisia and Algeria. Exclusively employing text in Ottoman Turkish, the map is traversed by hundreds of lines that connect every city and town of importance in the empire, representing the main land travel routes between these centres. Each segment is accompanied by a number that corresponds to the estimated average travel times between the points in hours (assuming travel by foot while marching, or travel with a horse at a slow trott). The travel times in hours roughly correspond to the distance in the Ottoman unit of a firsah (or league), which is equivalent to 5.685 km (3.532 miles). In the lower right, the map features a chart quantifying the routes between the most important centres. For instance, the map reveals that, on average, it took 18 hours to travel from the Red Sea port of Jeddah to the holy city of Mecca (a journey that would normally be divided into at least two, if not three, days). The present work is the first ever map to display the distances between all significant travel points in the Ottoman Empire, and for this reason it would have been vitally useful for soldiers, merchants and government bureaucrats when planning their itineraries. It was also one of the only maps to give an approximately accurate notion of the times and distance along several of the most important Hajj Routes, including the famous Syrian Hajj Road, being the 1307 km-long route from Damascus to Mecca, which is here measured out on the present map. The route itself is of such great historical significance that it is being considered by UNESCO for World Heritage Status, an unusual distinction for an itinerary, as opposed to a single, distinct place. Transportation had always been one of the great challenges confronting the Ottoman Empire. An astoundingly vast realm, spanning parts of three continents, and traversing some of the World’s most rugged and forbidding terrain, overland travel was especially difficult. Traditionally, the condition of the empire’s roads was deplorable; many places were connected only by crude caravan trails. For instance, before the introduction of railways, it took 14-16 days for a horse cart laden with produce to travel from Ankara to Istanbul, while the routes between centres even further part could take months to traverse. Throughout the 19th Century the territorial integrity of the empire was continually threatened and reduced by the Sublime Porte’s foreign and domestic enemies. The inability of the Ottoman Army to quickly deploy to military theatres severely limited the Sultan’s authority. Moreover, the extreme travel times between centres was hindering the empire’s ability to develop a modern industrial national economy, one of the government’s ultimate goals. Moreover, the empire was also home to Mecca and Medina, the two holiest sites of Islam, the latter of which was the destination of the Hajj, the world’s greatest pilgrimage. The Ottoman Sultan’s legitimacy rested upon his clam to being the Caliph of Islam, or the Defender of the Faith, which included a responsibility for the protection of pilgrims. As the routes to Mecca were often arduous, if not dangerous, this somewhat undercut the Sultan’s effectiveness as the ‘protector’, a matter which Abdul Hamid II would go to extraordinary efforts to ameliorate. Abdul Hamid II’s government relied heavily upon foreign capital and technical expertise to improve the country’s ports, build macadamized roads, and, most importantly, to create a comprehensive railway network. The present map depicts the rapidly expanding Ottoman railway system, just after a wave of development had revolutionized travel in the empire’s European domains, but just before an unprecedented boom in railway construction would do the same for Ottoman Asia. As shown, the Balkans are traversed by several railways; most notably as of 1888 the great port of Salonika (Thessaloniki) was connected to the rest of Europe by rail, while Istanbul was linked to the European system for the first time that same year, providing the direct route for the famed Orient Express, which commenced in 1889. One will also notice the first great leg of the Anatolian Railway that connected Istanbul to Ankara on December 31, 1892, completed only a matter of weeks before the present map was issued. The Anatolian Railway would subsequently be expanded with the ambition of reaching Iraq, creating the Baghdad Railway (a project which would become one of the great factors of World War I). The present map, however, predates the great railway boom that would occur in the Levant and Arabia, whereby from 1895 to 1908, major centres in Syria, Lebanon and Palestine would be linked, while the legendary Hejaz Railway would connect Damascus to Medina (within relatively close proximity to Mecca). The railways had a revolutionary effect upon the Ottoman Empire, spurring economic development, improving governance and facilitating military movement. The empire’s infrastructure projects and related economic development, administrative and military ventures were a catalyst leading to the creation of advanced thematic cartography in Istanbul. The Sublime Porte’s various organs (notably the War Ministry) provided generous funding for the creation of maps to assist the modernization of the country and the graphic recording of data. This dovetailed into the rise of a vibrant private publishing scene that enjoyed government patronage. Ottoman cartographers were initially schooled in the world’s most advanced cartographic methods by French and German instructors (while some Ottoman mapmakers even apprenticed in European geographic publishing houses), although by the late 1880s many Ottoman cartographers had gained the skills and experience to develop their own unique works with an Ottoman flair, well beyond duplicating Western methods. Ottoman cartographers were producing topographic and thematic maps of the highest sophistication and diversity, every bit as impressive as those of the best German and French and British mapmakers. However, these works, such as the present map, are today not nearly as well-known as they deserve to be. First, Ottoman thematic maps tend to be very rare today. They were almost invariably issued in only small print runs, while maps intended for practical use in the field, such as the present work, tended to perish, leaving few survivors. Second, Turkey’s switch from using Arabic-based script to Latin script, in 1928, ensured that many of the surviving Ottoman maps were discarded, as they could no longer be understood my most people. Third, the academic study of late Ottoman cartography, even in Turkey, has been haphazard, leaving many important realms of the subject almost completely untouched by modern authors. Hopefully, the present rise in interest in Ottoman cartography will lead to these maps receiving the attention they deserve viz. better known Western works. The present map is rare. While encountered another example a few years ago, the map only rarely appears on the market. We cannot trace any examples in institutions outside of Turkey. The library of the Harita Genel Müdürlüğü (General Command of Mapping) of the Turkish Army, in Ankara, holds an example that that has appeared as part of exhibitions." (Alexander Johnson, 2020)

This service was last updated September 2016. This map service draws attention to your thematic content by providing a neutral background with minimal colors, labels, and features. Only key information is represented to provide geographic context, allowing your data to come to the foreground. This light gray basemap supports any strong colors and labels for your theme, creating a visually compelling map graphic which helps your reader see the patterns intended. See these blog posts for more information on how to use this map: Esri Canvas Maps Part I: Author Beautiful Web Maps With Our New Artisan Basemap Sandwich and Esri Canvas Maps Part II: Using the Light Gray Canvas Map effectively. The map shows populated places, water, roads, urban areas, parks, building footprints, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority. This map was compiled by Esri using HERE data, DeLorme basemap layers, MapmyIndia data, and Esri basemap data. The basemap includes boundaries, city labels and outlines, and major roads worldwide from 1:591M scale to 1:72k scale. More detailed nationwide coverage is included in North America, Europe, Africa, South America and Central America, the Middle East, India, Australia, and New Zealand to be fully consistent with the World Street Map and World Topo map down to the 1:9k scale. Data for select areas of Africa and Pacific Island nations from ~1:288k to ~1:9k was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.In addition, some of the data in the World Light Gray Base map service has been contributed by the GIS community. You can contribute your data to this service and have it served by Esri. For details, see the Community Maps Program. For details on data sources in this map service, view the list of Contributors for the World Light Gray Base map.View the coverage map below to learn more about the levels of detail:World coverage map: Shows the levels of detail throughout the world. The World Light Gray Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the Light Gray Base with the Reference service drawn on top. This sample web map contains several examples of thematic content in the light gray canvas basemap with its reference overlay. Note: This map service is not supported in ArcGIS for Desktop 9.3.1 or earlier because it uses the mixed format cache format. Scale Range: 1:591,657,528 down to 1:9,028Coordinate System: Web Mercator Auxiliary Sphere (WKID 102100)Tiling Scheme: Web Mercator Auxiliary SphereMap Service Name: World_Light_Gray_Base

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 102 atolls of France (in the Pacific and Indian Oceans) as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW.

Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product.

Soil data confidence is described using a 4 class system between high and very low as outlined below.:

• Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps.

• Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only.

Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area.

Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

This dataset contains raster data, R scripts, and obtained results that are related to statistically rigorous methods for accuracy assessment and area estimation of forest change maps. These data can be used to run all simulations, comparisons, and examples described in RELATED MATERIALS 1. The R scripts can also be used for the accuracy assessment of thematic maps derived from other datasets.

The dataset presents the estimated occurrence of less common tree species (other than pine and spruce) in the form of thematic maps covering entire area of Finland. The maps series represent the following years: 1994, 2002, 2009 and 2015. The tree species maps are based on geostatistical interpolation of field measurements from national forest inventory sample plots and satellite image-based forest resource estimates. The occurrence data is presented as the average volume (m3/ha) of the tree species in forestry land. The tree species maps are available as ESRI polygon shapefiles where Finland is divided into 1 x 1 km2 square polygons for which the tree species data is estimated. Koordinaattijärjestelmä: ETRS89 / ETRS-TM35FIN (EPSG:3067)

Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.

Abstract Easily understandable thematic maps of geo-scientific parameters are important for land use decision making. If several parameters are relevant and have to be compared, it is important that they are consistent with each other, available at the same spatial range and detail and normed to a common data range. In the current study, geological and topographical data have been used to derive a set of 90 geo-scientific maps for an area of 400 km² in the northern part of the metropolitan area of Belo Horizonte. Each parameter has been transferred to a common data range between 0 and 1 using a Semantic Import Model strategy and afterwards combined to derive new parameters for soil hydrology and hydrogeology. From these, many intermediate geo-scientific parameters, maps of geo-resources (sand/gravel, carbonates, fertile soils) and geo-hazards (erosion, groundwater pollution) have been derived that they can be used as base information for a participatory and sustainable land use planning. The workflow is transparently stored in GIS-tools and can be modified and updated if new information is available.

This dataset contains two .zip files that hold supplementary data and information related to a review article "Area estimation and accuracy assessment for forest change maps derived from satellite data" by Shimizu (2023) in Journal of the Japanese Forestry Society.

The first .zip file contains supplementary figures and tables, which are almost identical to those in the review article.

The second .zip file includes raster data, R scripts, and obtained results. These data can be used to run all simulations, comparisons, and examples described in the review article. The R scripts can also be used for the accuracy assessment of thematic maps derived from other datasets.

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 52 atolls of the Indian Ocean and Red Sea as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per country or region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).