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.

An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. However, due to partial observability, agents often struggle to determine optimal strategies. Thus, developing a robust information fusion method is crucial for addressing these challenges. Information fusion expands the observation range of each agent, thereby enhancing the overall performance of the MAPF system. This paper explores a fusion approach in both temporal and spatial dimensions based on Graph Attention Networks (GAT). Since MAPF is a long-horizon, continuous task, leveraging historical observation dependencies is key for predicting future actions. Initially, historical observations are fused by incorporating a Gated Recurrent Unit (GRU) with a Convolutional Neural Network (CNN), extracting local observations to form an encoder. Next, GAT is used to enable inter-agent communication, utilizing the stability of the scaled dot-product aggregation to merge agents’ information. Finally, the aggregated data is decoded into the agent’s final action strategy, effectively solving the partial observability problem. Experimental results show that the proposed method improves accuracy and time efficiency by 24.5%, 47%, and 37.5%, 73% over GNN and GAT, respectively, under varying map sizes and agent densities. Notably, the performance enhancement is more pronounced in larger maps, highlighting the algorithm’s scalability.

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.

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 July of 2025.

An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. However, due to partial observability, agents often struggle to determine optimal strategies. Thus, developing a robust information fusion method is crucial for addressing these challenges. Information fusion expands the observation range of each agent, thereby enhancing the overall performance of the MAPF system. This paper explores a fusion approach in both temporal and spatial dimensions based on Graph Attention Networks (GAT). Since MAPF is a long-horizon, continuous task, leveraging historical observation dependencies is key for predicting future actions. Initially, historical observations are fused by incorporating a Gated Recurrent Unit (GRU) with a Convolutional Neural Network (CNN), extracting local observations to form an encoder. Next, GAT is used to enable inter-agent communication, utilizing the stability of the scaled dot-product aggregation to merge agents’ information. Finally, the aggregated data is decoded into the agent’s final action strategy, effectively solving the partial observability problem. Experimental results show that the proposed method improves accuracy and time efficiency by 24.5%, 47%, and 37.5%, 73% over GNN and GAT, respectively, under varying map sizes and agent densities. Notably, the performance enhancement is more pronounced in larger maps, highlighting the algorithm’s scalability.

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.

This study attempts to demonstrate the significance of considering two-way relationships by proposing a keyword network formed using bidirected graphs and association rules to examine the two-way relationship of two or more keywords. A web application to visualize is accessible at www.coconut-libtool.com

Publication of a map of the Catalonia road network, with the Milestones.

The BOREAS TE-23 team collected map plot data in support of its efforts to characterize and interpret information on canopy architecture and understory cover at the BOREAS tower flux sites and selected auxiliary sites from May to August 1994. Mapped plots (typical dimensions 50 m x 60 m) were set up and characterized at all BOREAS forested tower flux and selected auxiliary sites. Detailed measurement of the mapped plots included 1) stand characteristics (location, density, basal area); 2) map locations DBH of all trees; 3) detailed geometric measures of a subset of trees (height, crown dimensions); and 4) understory cover maps. The data are stored in tabular ASCII files.

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}
}

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.

European Union Imports of maps, hydrographic or similar charts (printed) from El Salvador was US$39 during 2024, according to the United Nations COMTRADE database on international trade. European Union Imports of maps, hydrographic or similar charts (printed) from El Salvador - data, historical chart and statistics - was last updated on August of 2025.

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

Dataset Details

The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:

• Subject matter triples file
  • fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.
    • Example of a row in train.txt, valid.txt, and test.txt:
      • 2, 192, 0
    • Example of a row in entity2id.txt:
      • /g/112yfy2xr, 2
    • Example of a row in relation2id.txt:
      • /music/album/release_type, 192
    • Explaination
      • "/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.
• Type system file
  • freebase_endtypes: Each row maps an edge type to its required subject type and object type.
    • Example
      • 92, 47178872, 90
    • Explanation
      • "92" and "90" are the type id of the subject and object which has the relationship id "47178872".
• Metadata files
  • object_types: Each row maps the MID of a Freebase object to a type it belongs to.
    • Example
      • /g/11b41c22g, /type/object/type, /people/person
    • Explanation
      • The entity with MID "/g/11b41c22g" has a type "/people/person"
  • object_names: Each row maps the MID of a Freebase object to its textual label.
    • Example
      • /g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en
    • Explanation
      • The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.
  • object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.
    • Example
      • /m/05v3y9r, /type/object/id, "/music/live_album/concert"
    • Explanation
      • The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.
  • domains_id_label: Each row maps the MID of a Freebase domain to its label.
    • Example
      • /m/05v4pmy, geology, 77
    • Explanation
      • The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.
  • types_id_label: Each row maps the MID of a Freebase type to its label.
    • Example
      • /m/01xljxh, /government/political_party, 147
    • Explanation
      • The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.
  • entities_id_label: Each row maps the MID of a Freebase entity to its label.
    • Example
      • /g/11b78qtr5m, Viroliano Tries Jazz, 2234
    • Explanation
      • The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.
    • properties_id_label: Each row maps the MID of a Freebase property to its label.
      • Example
        • /m/010h8tp2, /comedy/comedy_group/members, 47178867
      • Explanation
        • The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.
    • uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.
      • Example
        • uri_original2simplified
          • "http://rdf.freebase.com/ns/type.property.unique": "/type/property/unique"
        • uri_simplified2original
          • "/type/property/unique": "http://rdf.freebase.com/ns/type.property.unique"
      • Explanation
        • The URI "http://rdf.freebase.com/ns/type.property.unique" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.
        • The identifier "/type/property/unique" in our dataset has URI http://rdf.freebase.com/ns/type.property.unique in the original Freebase RDF dataset.

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Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating indoor exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with laser range finders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph op-timization. It performs loop-closure detection and correction to recognize previously visited places and correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multi-layer LI-DAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicated that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling of the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31 m long ac-quisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multi-layer LIDAR-based macrodrone given the low deviation between the point clouds built by both drones. About 85 % of the cloud-to-cloud distances were less than 10 cm. Date Submitted: 2023-11-21

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.

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.

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.

An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. However, due to partial observability, agents often struggle to determine optimal strategies. Thus, developing a robust information fusion method is crucial for addressing these challenges. Information fusion expands the observation range of each agent, thereby enhancing the overall performance of the MAPF system. This paper explores a fusion approach in both temporal and spatial dimensions based on Graph Attention Networks (GAT). Since MAPF is a long-horizon, continuous task, leveraging historical observation dependencies is key for predicting future actions. Initially, historical observations are fused by incorporating a Gated Recurrent Unit (GRU) with a Convolutional Neural Network (CNN), extracting local observations to form an encoder. Next, GAT is used to enable inter-agent communication, utilizing the stability of the scaled dot-product aggregation to merge agents’ information. Finally, the aggregated data is decoded into the agent’s final action strategy, effectively solving the partial observability problem. Experimental results show that the proposed method improves accuracy and time efficiency by 24.5%, 47%, and 37.5%, 73% over GNN and GAT, respectively, under varying map sizes and agent densities. Notably, the performance enhancement is more pronounced in larger maps, highlighting the algorithm’s scalability.