Author: ANN WURST, NGS TEACHER CONSULTANTGrade/Audience: grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: activitySubject topic(s): cartography, maps, regional geographyRegion: worldStandards: TEXAS TEKS (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions; (B) create a product on a contemporary government issue or topic using critical methods of inquiry; (D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference; Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for world geo, AP Human Geo and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of cartography/map making , can be applied in every unit where map skills are needed. Helps further critical thinking skills.

The Niobe Aphrodite Map Area covers over 25% of the surface of Venus and extends from 57N to 57S and 60E to 180E. The structural-element map presented here is derived from the1:10 M-scale geologic maps of Niobe Planitia, U.S. Geological Survey I-2467 and Aphrodite Terra, U.S. Geological Survey I-2476. Both maps are in various stages of review and revision overseen by the U.S. Geological Survey on behalf of NASA.

Here we present a Geographic Information System (GIS) that contain the different structural elements of the area (deformation structures and lithodemic units), that can be used to analyze relationships between and among suites of structural elements across this large portion of Venus’ surface.

Base images and data on which determination of the structural element determination is based can be accessed and downloaded directly in GIS-ready formats through the USGS Map a Planet website (https://astrogeology.usgs.gov/tools/map-a-planet-2).

The project “Basic Geological Charter of Sardinia on a scale of 1:25,000” aims to create a geological map that is homogeneous and extended to the whole island, adapted to the planning objectives of the Regional Landscape Plan (PPR) and in accordance with the indications of the Geological Service of Italy. The geology was represented at 1:25,000, a scale of compromise between the unevenness of the basic data and the need to have a unique and homogeneous cartography for the entire island (58 Sheets in scale 1:50,000, comprising 197 Sections in scale 1:25,000).

UI Elements Learn O0nqe 95%mAP

## Overview

UI Elements Learn O0nqe 95%mAP is a dataset for object detection tasks - it contains UI Elements FkF7 annotations for 4,752 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Linear elements of the 2008 Land Use Map. The linear entities represent hydrographic and road elements with a width of less than 25 m. The figure was created following the update of the land use map created in 2003.

The “Protected Small Landscape Element” map is included in the Environment Regulation. On this are the most special, often old landscape features of the province of Utrecht. These elements are protected and should not be cut down. In 2021, this map was updated with elements in the municipality of Vijfheerenlanden and a number of new elements in the rest of the province. The Environment Regulation has been adopted but will only work after the Environment Act enters into force.Until then the map from the Interim Regulation is active.

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.ArcGIS INSPIRE Open Data is a lightweight solution for European public sector organizations implementing the INSPIRE and PSI-2/Open Data Directives. See the Getting to know ArcGIS INSPIRE Open Data story map to learn more.Geodatabase (GDB) templates are available on the ArcGIS INSPIRE Open Data demonstration Hub. INSPIRE Alternative Encoding documentation on GitHub is publicly available per the Implementing Rules on interoperability of spatial data sets and services (Commission Regulation (EU) No 1089/2010). These resources are provided as-is and are freely available.

Legacy product - no abstract available Legacy product - no abstract available

Map file package containing shaded relief base with Hot Pot project area, major roads, railroads, and rivers. The inset map shows regional Paleozoic structural elements.

Representation of structural features and in particular faults and oversurges.

The ubiMap dataset is comprised of 3,530 map images collected from the Bing image search service (1,730 maps) and Geo-Journal (1,800 maps). Each image has been manually labeled with 22 types of map elements, including their boundary shapes and category properties, resulting in an average of 5.92 elements per map. ubiMap-l is built uopon ubiMap by removing maps that contained only one element, which results a total of 3,515 maps for map layout retrieval test. We first opensourced 703 maps in ubiMap-l that we used for testing our map layout representation learning framework, MapLayNet. Besides 703 map images and their layout label data, embedding of MapLayNet and its baseline model is provided along with the python codes for embedding visualizaiton. Please cite the paper if you use the dataset. Yang, J., Chen, C., Jia, F., Xie, X., Fang, L., Wang, G., & Meng, L. (2025). MapLayNet: map layout representation learning using weakly supervised structure-aware graph neural networks. Cartography and Geographic Information Science, 1–22. https://doi.org/10.1080/15230406.2025.2533316

A series of element maps collected using the Maia 384 detector array on the XFM beamline at the Australian Synchrotron. Maps are both single element images and RGB composites (indicated by file name). Lower resolution versions of these files form part of a figure 1 in a paper on the application of the Maia mapping technique and are made available here to allow the full resolution images (1:1 binning of pixels) to be accessed. Lineage: X-ray spectra were collected using the Maia 384 detector on the XFM beamline at the Australian Synchrotron. The spectral data were collected with an incident beam energy of 18.5 keV, a pixel size of 4 µm and dwell times per pixel of 0.97 msec. The Maia XFM full spectral data were analysed using the GeoPIXE software suite which uses a fundamental parameters approach, with spectral deconvolution and imaging using the Dynamic Analysis method based on fitting a representative total spectrum, and a detailed model of Maia detector array efficiency. Spectra are fitted using a X-ray line relative intensities that reflect integration of yields and X-ray self-absorption effects for the given matrix or mineral phase and the contrasting efficiency characteristics across the detector array. The result is a matrix transformation that allows projection of the full-spectral data into element maps in this collection.

Author: J. Cain, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 4Resource type: lessonSubject topic(s): mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 2: People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context. Objectives: Students will be able to:

1. Explore a variety of maps.

2. Become acquainted with the elements of maps referred to as TODALS:

3. Title

4. Orientation

5. Date

6. Author

7. Legend (Key)

8. Scale

9. Locate and interpret TODALS from a variety of maps.

10. Compare and contrast elements of given maps while looking for bias.

11. Reflect on the importance of knowing TODALS when understanding and interpreting maps. Summary: Basic mapping terminology is essential for understanding and interpreting various types of maps. Knowing where to find these essential elements, and interpreting their meaning, are critical to the development of a 4th grader’s knowledge of geography.

Geo-referenced vector-type database, containing geomorphological and anthropogenic elements in linear form, collected as part of the national geological mapping project (CARG) at the 1: 25.000 acquisition scale and reviewed at regional level. The geographical area covered comprises the sheets on a scale of 1: 50.000 in which the regional territory falls.

The Geological Atlas of the Western Canada Sedimentary Basin was designed primarily as a reference volume documenting the subsurface geology of the Western Canada Sedimentary Basin. This GIS dataset is one of a collection of shapefiles representing part of Chapter 12 of the Atlas, Devonian Woodbend-Winterburn Strata of the Western Canada Sedimentary Basin, Figure 31, Winterburn Group Depositional Elements and Index Map. Shapefiles were produced from archived digital files created by the Alberta Geological Survey in the mid-1990s, and edited in 2005-06 to correct, attribute and consolidate the data into single files by feature type and by figure.

OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses. Browse the map of London on OpenStreetMap.org Downloads: The whole of England updated daily: england.osm.bz2 ~185M - .osm formatted raw XML data (compressed) england.shp.zip ~156M - ESRI shapefiles For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki. Data access APIs: Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square: http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969 Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket: http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70] The .osm format The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

Vector-type georeferenced database containing the geomorphological and anthropological elements in polygonal form, collected in the framework of the National Geological Cartography (CARG) project at the 1:25,000 acquisition scale and revised at regional level. The geographical area covered includes the sheets on a scale of 1:50,000 in which the regional territory falls.

This dataset (lineaments_ln_ll.shp) comprises structural features compiled into GIS format from existing literature, published up to 2003. The data represent fault/lineament locations known or inferred in the Alberta Plains. We have chosen to digitize and publish all lineaments from source maps even where they extended beyond the Alberta boundary. Each compiled feature is characterized by a set of attributes including: affected formations (oldest affected and oldest non-affected stratigraphic unit), fault type, fault sense of displacement, evidence used to infer the fault/lineament, original reference information and publication scale, and an estimate of the georeferencing error. The completeness of the captured attribute set varies for each feature as a function of the level of detail in the source article. The data set should be used cautiously. First, the original authors' interpretation of subsurface faults, particularly of 'basement faults', from air photo or satellite imagery lineaments is tenuous. Second, the vast majority of faults inferred in the foreland basin (Alberta Plains) east of the deformation front are normal-slip faults. although only the dip slip component has been inferred, some of these faults may also have a strike-slip component, generally not accounted for. Third, the location of lineaments includes cumulative errors inherent in the process of transferring into GIS lineaments traced by hand in the pre-computer era on small scale (regional) paper-copy maps. Such errors include spatial imprecisions in original lineament identification and drawing and errors in georefencing of the source map, as well as minor errors introduced during lineament digitization. Although each of them is minor at the scale of the original map, the cumulative effect of these errors may be significant and even misleading for large-scale (township or larger) projects.

Constructing maps suitable for autonomous vehicles (AVs) is a critical research focus in autonomous driving and AI, extending cartography’s challenges. Building on cartographic principles, we propose the concept of a road scene map along with its modeling method that incorporates dynamic/static traffic elements with geometric/semantic features. Current limitations include unclear road scene graph relationships and a lack of integration among 3D traffic entity detection, map element detection, and scene relation extraction. To address these issues, we propose a method for constructing road scene maps: (1) A multi-task detection model identifies traffic entities and map elements directly in bird’s-eye-view (BEV) space, providing precise location, geometry, and attribute data; (2) A unified road scene relation pattern enables rule-based spatial/semantic relationship extraction. Experiments on nuScenes demonstrate improvements: the detection model achieves 1.5% and 1.9% accuracy gains in traffic entity and map element detection over state-of-the-art methods, while the relation extraction method covers broader perceptual ranges and more complex interactions. Results confirm the effective integration of 3D object detection, map element recognition, and scene relation extraction into a unified map. This integration delivers critical environmental information (locations, geometries, attributes, and spatial/semantic relationships) to AVs, significantly enhancing their perception and reasoning in dynamic road scenarios.