Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

Namibia Imports of maps, hydrographic or similar charts (printed) from Spain was US$1.11 Thousand during 2019, according to the United Nations COMTRADE database on international trade. Namibia Imports of maps, hydrographic or similar charts (printed) from Spain - data, historical chart and statistics - was last updated on November of 2025.

Additional file 1. Perl script used for converting a contact map into an adjacency matrix based on the graphrepresentation in Fig. 1a.

The Historical Map and Chart Collection of the Office of Coast Survey contains over 35000 historical maps and charts from the mid 1700s up through the 2020s, including the final cancelled editions of NOAA's raster charts. These images are available for viewing or download through the image catalog at https://historicalcharts.noaa.gov/. The Collection includes some of the nation's earliest nautical charts, hydrographic surveys, topographic surveys, bathymetric maps, annual reports, coast pilots, geodetic surveys, city plans, and Civil War battle maps. The Collection is a rich primary historical archive and a testament to the artistry of copper plate engraving technology of the nineteenth and twentieth centuries. Notable offerings include maps of Vancouver's explorations, the "Wilkes Atlas" of the U.S. Whistler's Anacapa Island chart, an extensive Civil War collection, a large scale topographic series of Washington, D.C., city plans, the reengraving of the famous 1792 L'Enfant and Ellicott plan for Washington D.C., and many artistic perspective sketches that were once an integral part of hydrographic surveys and published charts.

Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data was reported at 0.014 USD mn in Jan 2025. This records an increase from the previous number of 0.014 USD mn for Dec 2024. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data is updated monthly, averaging 0.012 USD mn from Apr 2022 (Median) to Jan 2025, with 34 observations. The data reached an all-time high of 0.028 USD mn in Aug 2023 and a record low of 0.005 USD mn in Mar 2023. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH147: Foreign Trade: by HS 8 Digits: Import: HS49: Printed Books, Newspapers, Pictures, and Other Products of Printing Industry, Manuscripts, Typescripts, and Plans.

Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Large-scale DLG data are derived from USGS 1:20,000-, 1: 24,000-, and 1: 25,000-scale 7.5-minute topographic quadrangle maps and are available in nine categories: (1) hypsography, (2) hydrography, (3)vegetative surface cover, (4) non-vegetative features, (5) boundaries, (6)survey control and markers, (7) transportation, (8) manmade features, and (9)Public Land Survey System. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

GLOBE provides the ability to view and interact with data measured across the world. Select the visualization tool to map, graph, filter and export data that have been measured across GLOBE protocols since 1995. Currently the GLOBE Data Visualization Tool supports a subset of protocols. Additional Features and capabilities are continually being added.

Summary:

A sub-set of the Gaia Data Release 3 data centered on the Sun for use in mapping the local Galaxy. The data includes three columns for each star: parallax, heliocentric longitude, and heliocentric latitude. Data can be converted to Galactocentric Rectangular Coordinate (X, Y, Z) or Galactocentric Cylindrical Coordinate (R, Phi, Z). PLEASE NOTE: There are many incorrectly measured parallaxes -- all negative parallaxes must be removed.

Columns:

• parallax (units: mas) - Parallactic angle (used for determining the distance to a star).
• longitude (units: degrees) - The heliocentric longitude, with zero degrees corresponding to the direction of the Galactic Center.
• latitude (units: degrees) - The heliocentric latitude, with +90 degrees corresponding to the North Galactic Pole and 0 degrees corresponding to the Galaxy's mid-plane.

Query from Gaia Database:

SELECT gaia_source.parallax, gaia_source.l, gaia_source.b

FROM gaiadr3.gaia_source 

WHERE 

gaia_source.random_index < 5000000 AND

gaia_source.phot_g_mean_mag BETWEEN 14 AND 18 AND

gaia_source.bp_rp BETWEEN 0.5 AND 2.5 AND

(1.0 / gaia_source.parallax) * COS(RADIANS(gaia_source.b)) < 0.250

Note the final condition in the query limits the selection of stars to those within 250 parsecs (in-plane distance) of the Sun. In other words, we are examining the stars in a cylinder of radius 250 parsecs centered on the Sun, punching perpendicularly through the Milky Way disk.

License:

The Gaia Data is under the following license: Open Source With Attribution to ESA/Gaia/DPAC, reproduced here:

"The Gaia data are open and free to use, provided credit is given to 'ESA/Gaia/DPAC'. In general, access to, and use of, ESA's Gaia Archive (hereafter called 'the website') constitutes acceptance of the following general terms and conditions. Neither ESA nor any other party involved in creating, producing, or delivering the website shall be liable for any direct, incidental, consequential, indirect, or punitive damages arising out of user access to, or use of, the website. The website does not guarantee the accuracy of information provided by external sources and accepts no responsibility or liability for any consequences arising from the use of such data."

All of my course materials are free to use with attribution as well.

This is the data and codes that support the findings of the IJGIS paper "Aligning geographic entities from historical maps for building knowledge graphs".

Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth.

The Marine Maps and Chart Archive held by BGS contains maps created by BGS (the majority of which result from BGS offshore mapping projects) and also maps acquired from various other sources (e.g. UK Hydrographic Office and MCA Civil Hydrography Programme survey charts). The maps which date from the 1960s onwards are very variable in subject type and scale ranging from survey navigation to geological interpretation. The maps are primarily for the UKCS (United Kingdom Continental Shelf). The coverage of some map types is the entire UKCS whilst other have only regional or localised extent. The maps which are a mix of paper and digital are applicable to a wide range of uses including environmental, geotechnical, geophysical and geological studies. range of uses including environmental, geotechnical, geophysical and geological studies. Scanned maps can be viewed via the BGS maps portal http://www.bgs.ac.uk/data/maps.

Sketched graphics depicting map outlines. Graphics produced by C. Mary Brake, Reflection Graphics for the NZ Garden Bird Survey as part of the 'Building Trustworthy Biodiversity Indicators' project funded by the Ministry for Business, Innovation and Employment.

This web map depicts the results of the 2021 Alaska Coastal & Ocean Mapping Prioritization Survey where respondents noted they have a priority requirement for nautical charts or mapping updates. The survey question asked what Map Product they need for a given cell, and this map shows all cells where the answer was equal to Nautical Maps/Charts. Used in the experience builder here: https://experience.arcgis.com/experience/0b3e823a5c8b42dcbbd1b49f91d4c07aHosted on the Alaska Coastal Mapping Strategy Hub Site: https://alaska-coastal-mapping-strategy-noaa.hub.arcgis.com/

The dataset

The dataset is produced within the SafeLog project and it is used for benchmarking of multi-agent path planning algorithms. Specifically, the dataset consists of a set of 21 maps with increasing density and a set of 500 random assignments, each for a group of 100 agents for planning on each of the maps.

All of the maps, in the form of a graph G = {V, E}, are built on the same set of 400 vertices V. The sets of edges Ej, where j ∈ (0; 20), in the maps then form a set ranging from a spanning tree to a mostly 4-connected graph. These maps were created by generating a complete square graph with the size of 20*20 vertices. The graph was then simplified to a spanning tree, and, finally, approximately 50 random edges from the complete graph were added 20 times, to create the set of 21 maps of density ranging from 800 to 1500 edges in the graph.

Content and format

The following files are included in the dataset

test_nodes.txt - 400 nodes of a 20*20 square map in the form "id x y"
testAssignment.txt - 50499 random pairs of nodes ids from test_nodes.txt
test_edgesX.txt - pairs of adjacent nodes ids from test_nodes.txt forming edges
- X = 0 - tree
- X = 20 - full graph
- created starting at a full graph and repeatedly erasing edges until a tree remains

To illustrate the maps in the dataset, we provide three images (1008.png, 1190.png, and 1350.png) showing maps with 1008 (1190, 1350) edges.

Citation

If you use the dataset, please cite:

[1] Hvězda, J., Rybecký, T., Kulich, M., and Přeučil, L. (2018). Context-Aware Route Planning for Automated Warehouses. Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

@inproceedings{Hvezda18itsc,
author = {Hvězda, Jakub and Rybecký, Tomáš and Kulich, Miroslav and Přeučil, Libor},
title = {Context-Aware Route Planning for Automated Warehouses},
booktitle = {Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC)},
publisher = {IEEE Intelligent Transportation Systems Society},
address = {Maui},
year = {2018},
doi = {10.1109/ITSC.2018.8569712},
}

[2] Hvězda, J., Kulich, M., and Přeučil, L. (2019). On Randomized Searching for Multi-robot Coordination. In: Gusikhin O., Madani K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham.

@inbook{Hvezda19springer,
author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},
title = {On Randomized Searching for Multi-robot Coordination},
booktitle = {Informatics in Control, Automation and Robotics},
publisher = {Springer},
address = {Cham, CH},
year = {2019},
series = {Lecture Notes in Electrical Engineering},
language = {English},
url = {https://link.springer.com/chapter/10.1007/978-3-030-31993-9_18},
doi = {10.1007/978-3-030-31993-9},
}

[3] Hvězda, J., Kulich, M., and Přeučil, L. (2018). Improved Discrete RRT for Coordinated Multi-robot Planning. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2).

@inproceedings{Hvezda18icinco,
author = {Hvězda, Jakub and Kulich, Miroslav and Přeučil, Libor},
title = {Improved Discrete RRT for Coordinated Multi-robot Planning},
booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - (Volume 2)},
publisher = {SciTePress},
address = {Madeira, PT},
year = {2018},
language = {English},
url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=ppwUqsGaX18=\&t=1},
doi = {10.5220/0006865901710179},
access = {full}
}

eXtension Foundation, the University of New Hampshire, and Virginia Tech have developed a mapping and data exploration tool to assist Cooperative Extension staff and administrators in making strategic planning and programming decisions. The tool, called the National Extension Web-mapping Tool (or NEWT), is the key in efforts to make spatial data available within cooperative extension system. NEWT requires no GIS experience to use. NEWT provides access for CES staff and administrators to relevant spatial data at a variety of scales (national, state, county) in useful formats (maps, tables, graphs), all without the need for any experience or technical skills in Geographic Information System (GIS) software. By providing consistent access to relevant spatial data throughout the country in a format useful to CES staff and administrators, NEWT represents a significant advancement for the use of spatial technology in CES. Users of the site will be able to discover the data layers which are of most interest to them by making simple, guided choices about topics related to their work. Once the relevant data layers have been chosen, a mapping interface will allow the exploration of spatial relationships and the creation and export of maps. Extension areas to filter searches include 4-H Youth & Family, Agriculture, Business, Community, Food & Health, and Natural Resources. Users will also be able to explore data by viewing data tables and graphs. This Beta release is open for public use and feedback. Resources in this dataset:Resource Title: Website Pointer to NEWT National Extension Web-mapping Tool Beta. File Name: Web Page, url: https://www.mapasyst.org/newt/ The site leads the user through the process of selecting the data in which they would be most interested, then provides a variety of ways for the user to explore the data (maps, graphs, tables).

The NOAA Chart Display Service (NCDS) renders NOAA electronic navigational chart (NOAA ENC®) data with “traditional paper chart” symbology in online and offline applications for which a basemap of nautical chart data is desired, including GIS, web-based, and mobile mapping applications.The service uses symbols, labels, and color schemes familiar to those who have used NOAA paper nautical charts or the NOAA Custom Chart application. NCDS is available as Esri REST Map Service, OGC Web Map Service (WMS), and MBTiles formats.The ENC data in the service are updated weekly and include all of the latest Notice to Mariners corrections.

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Ohio Digital Raster Graphs (DRGs)

Statistics illustrates consumption, production, prices, and trade of Maps and hydrographic or similar charts; (printed other than in book form), including wall maps, topographical plans and similar in Belgium from 2007 to 2024.

Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth.