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.

Abstract The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for carbonatite-related rare earth element mineral systems in Australia. Maps showing the potential for carbonatite-related rare earth element (REE) mineral systems in Australia. Model 1 integrates three components: sources of metals, energy drivers, and lithospheric architecture. Supporting datasets including the input maps used to generate the mineral potential maps, an assessment criteria table that contains information on the map creation, and data uncertainty maps are available here Uncertainty Maps. The data uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. Map images provided in the extended abstract have the same colour ramp and equalised histogram stretch, plus a gamma correction of 0.5 not present in the web map service maps, which was applied using Esri ArcGIS Pro software. The extended abstract is avalable here Alkaline Rocks Atlas Legend

Currency Date modified: 16 August 2023 Next modification date: As Needed Data extent Spatial extent North: -9° South: -44° East: 154° West: 112° Source Information Catalog entry: Carbonatite-related rare earth element mineral potential maps Lineage Statement Product Created 20 April 2023 Product Published 16 August 2023 A large number of published datasets were individually transformed to summarise our current understanding of the spatial extents of key mineral system mappable criteria. These individual layers were integrated using statistically derived importance weightings combined with expert reliability weightings within a mineral system component framework to produce national-scale mineral potential assessments for Australian carbonatite-related rare earth element mineral systems. Contact Geoscience Australia, clientservices@ga.gov.au

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.

A series of element maps collected using the Maia 384 detector array on the XFM beamline at the Australian Synchrotron. Maps are RGB composites (indicated by file name) or a combination of black and white intensity with RGB. Lower resolution versions of these files are used in a paper on the application of microanalysis techniques in ore geology 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.

The knowledge of the geology of our territory and the processes that determine its evolution is a factor of great importance, always recognised as a priority by the Administration of the Autonomous Province of Trento. The geological map is made by combining different information. The structural element level is part of this information and is used to indicate the different types of traces of axial surfaces of the plicative structures.

Two active landslides at and near the retreating front of Barry Glacier at the head of Barry Arm Fjord in southern Alaska could generate tsunamis if they failed rapidly and entered the water of the fjord. Landslide A, at the front of the glacier, is the largest, with a total volume estimated at 455 M m3. Historical photographs from Barry Arm indicate that Landslide A initiated in the mid twentieth century, but there was a large pulse of movement between 2010 and 2017 when Barry Glacier thinned and retreated from about 1/2 of the toe of Landslide A. Interferometric synthetic aperture radar (InSAR) investigations of the area between May and November, 2020, revealed a second, smaller landslide (referred to as Landslide B) on the south-facing slope about 2 km up the glacier from Landslide A. Landslide-generated tsunami modeling in 2020 used a worst-case scenario where the entire mass of Landslide A (about 455 M m3) would rapidly enter the water. The use of multiple landslide volume scenarios in future tsunami modeling efforts would be beneficial in evaluating tsunami risk to communities in the Prince William Sound region. Herein, we present a map of landslide structures and kinematic elements within, and adjacent to, Landslides A and B. This map could form at least a partial basis for discriminating multiple volume scenarios (for example, a separate scenario for each kinematic element). We mapped landslide structures and kinematic elements at scale of 1:1000 using high-resolution lidar data acquired by the Alaska Division of Geological and Geophysical Surveys (DGGS) on June 26, 2020 and high resolution bathymetric data acquired by the National Oceanic and Atmospheric Administration (NOAA) in August, 2020. The predominate structures in both landslides are uphill- and downhill-facing normal fault scarps. Uphill-facing scarps dominate in areas where downslope extension from sliding has been relatively low. Downhill-facing scarps dominate in areas where downlslope extension from sliding has been relatively high. Strike-slip and oblique-slip faults form the boundaries of major kinematic elements. Four major kinematic elements, herein named the Kite, the Prow, the Core, and the Tail, are within, or adjacent to Landslide A. One major kinematic element, herein named the Wedge, forms Landslide B. Kinematic element boundaries are a result of cumulative, differential patterns and amounts of movement that began at inception of the landslides. Elements and/or their boundaries may change location as the landslides continue to evolve. Kinematic elements mapped in 2020 may or may not reflect patterns of historical short-term, episodic movement, or patterns of movement in the future. We were not able to field check our mapping in 2020 because of travel restrictions due to the COVID-19 pandemic. We hope to field check the mapping in the summer of 2021. In this data release, we include GIS files for the structural and kinematic map; metadata files for mapped structural features; and portable document files (PDFs) of a location map, and the structural and kinematic map at a scale of 1:5000. Lidar and bathymetric data used to map landslide structures will be released by DGGS and NOAA in 2021.

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.

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.

The "Basic Geological Map of Sardinia on a scale of 1:25,000" project aimed at creating a homogeneous geological map covering the whole island, suitable for the planning objectives of the Regional Landscape Plan (PPR) and compliant with the indications of the Geological Service of 'Italy. The geology was represented at 1:25,000, a compromise scale between the lack of homogeneity of the basic data and the need to have a single and homogeneous cartography for the entire island (58 Sheets at 1:50,000 scale, including 197 Sections at 1 :25,000).

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

Python tools to read, generate, and visualize the dataset,

3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

Benjamin Sertolli: Major contributions to our DevKit during his master thesis

Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

1. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

traffic signs,

traffic lights,

pole-like objects,

construction site locations,

construction site obstacles (point-like such as cones, and line-like such as fences),

line-shaped markings (solid, dashed, etc.),

polygon-shaped markings (arrows, stop lines, symbols, etc.),

lanes (ordinary and temporary),

relations between elements (only for construction sites, e.g., sign to lane association).

1. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

1. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

x-coordinates: 4 byte integer

y-coordinates: 4 byte integer

z-coordinates: 4 byte integer

intensity of reflected beams: 2 byte unsigned integer

ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

The financially constrained element of Visualize 2045 identifies all the regionally significant capital improvements to the region’s highway and transit systems that transportation agencies expect to make and to be able to afford through 2045.For more information on Visualize 2045, visit https://www.mwcog.org/visualize2045/.To view the web map, visit https://www.mwcog.org/maps/map-listing/visualize-2045-project-map/.* NOTE: the online map shows projects in the current version of the plan (2022 update); this data download is for the 2018 update to the plan.Adding GIS Data to ArcMap from a Map Package:To load the .mpk file if saved locally: From Windows Explorer1. Browse to the location of the .mpk file. 2. Double-click the file to launch ArcMap and unpack all the data in the package. From ArcCatalog1. Browse to the location of the .mpk file. 2. Right-click the file, and select Unpack. This action launches ArcMap and unpacks the data in the package. The process is the same if you are using ArcCatalog from within ArcMap.Note: The .mpk file cannot be opened within ArcMap.Regardless of where the .mpk file is stored originally, the data within the map package when unpacked saves on your hard drive in the Documents and Settings folder:C:\Documents_and_Settings\MyDocuments\ArcGIS\Packages*.gdb

Harford County Maryland 2016 Land Use Element Plan, E size PDF Map.

Click "Data Source" to download PDF map.

Concept Map resource is a statement of relationships from one set of concepts to one or more other concepts - either concepts in code systems, or data element/data element concepts, or classes in class models.

Polygonal elements of the 2008 Land Use Map. The polygonal elements represent land use elements with a width greater than 25 m. The figure was created following the update of the map relating to land use created in 2003. In the first half of 2009 it underwent some improvements for which a new version was derived starting from the 2008 edition It relates to land use, divided into legend classes (Corine Land Cover), for the polygons of the areas represented; it also contains linear thematic layers of traffic and hydrography. The legend, organized hierarchically according to the detailed classification of the five CORINE Land Cover categories up to 5 levels, has undergone some changes compared to the previous 2003 version. For the creation and updating of the land use map of the Autonomous Region of Sardinia, it was based on the following materials: Ikonos 2005-06 images, CTRN10k, toponymy, various data published on institutional thematic sites.

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.

Georeferenced vector-type database, containing the geomorphological and anthropic elements in precise form, surveyed within the national geological cartography project (CARG) at the acquisition scale of 1:25,000 and revised at the regional level. The geographic area covered includes the 1:50,000 scale sheets in which the regional territory falls.

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. The dataset is proposed by the upcoming CaGIS paper "MapLayNet: Map Layout Representation Learning using Weakly Supervised Structure-aware Graph Neural Networks"

FlowMapper.org is a web-based framework for automated production and design of origin-destination flow maps. FlowMapper has four major features that contribute to the advancement of existing flow mapping systems. First, users can upload and process their own data to design and share customized flow maps. The ability to save data, cartographic design and map elements in a project file allows users to easily share their data and/or cartographic design with others. Second, users can generate customized flow symbols to support different flow map reading tasks such as comparing flow magnitudes and directions and identifying flow and location clusters that are strongly connected with each other. Third, FlowMapper supports supplementary layers such as node symbols, choropleth, and base maps to contextualize flow patterns with location references and characteristics. Finally, the web-based architecture of FlowMapper supports server-side computational capabilities to process and normalize large flow data and reveal natural patterns of flows.

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).

The download service (OAF) maps of the mean element contents in the soil Brandenburg provides data for some selected environmentally relevant elements the maps of the mean contents (median values / P50) in the a) topsoil (OB) and in the b) subsoil (UG). The analytical data are based on approximately 2000 soil profiles recorded in accordance with soil mapping instructions (KA 5) and distributed irregularly over the country territory - royal water soluble contents in dry matter fine soil (less than 2 mm) for arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn); Total levels of mercury (Hg). The map is based on the legend units of the soil overview map, which were assigned to the grade classes corresponding to the medians for the dominant of the surface soil forms involved. The salary classes, which are uniform for OB and UG respectively, depend on the range of all values for the respective element. The legend units have been designed in such a way that the contents increase from green to yellow to brown. As there are only a few and highly scattering values for anthropogenic soils, settlement areas were assigned ‘no data’ (grey). The map representations are supplemented by a tabular overview of the average grades used and their color design. (see https://geo.brandenburg.de/karten/htdocs/2020_Elementgehalte.pdf) For more information, see the metadata of the data underlying the service. OGC API features is a web API that simplifies the use of data in appropriate web development environments. The API includes the following collection: Mean element content maps