The Map Reading Guide is an ideal resource for a wide range of map users and is an excellent and simplistic introduction to topographic maps which are suitable for anyone with an interest in maps. …Show full descriptionThe Map Reading Guide is an ideal resource for a wide range of map users and is an excellent and simplistic introduction to topographic maps which are suitable for anyone with an interest in maps. It contains: an explanation of what is a topographic map steps on how to read topographic maps, including explanations of map scale and how to use a map scale to calculate distance the differences between grid north, true north and magnetic north an explanation of projections and symbols used on topographic maps how hills and mountains are shown on maps using relief shading, hypsometric tinting, and contours what a datum is and why there are different datum explanations of the difference between geographic and grid coordinates how to quote grid references from topographic maps how to plan a successful trip using topographic maps using Global Navigation Satellite System (GNSS) receivers and magnetic compasses with topographic maps using a topographic map to find your current position and to set a course. This product is the guide/map romer card combination.

The Map Reading Guide is an ideal resource for a wide range of map users and is an excellent and simplistic introduction to topographic maps which are suitable for anyone with an interest in maps. It contains:- an explanation of what is a topographic map - steps on how to read topographic maps, including explanations of map scale and how to use a map scale to calculate distance- the differences between grid north, true north and magnetic north- an explanation of symbols used on topographic map symbols- how hills and mountains are shown on maps using relief shading, hypsometric tinting, and contours- what a datum is and why there are different datum- explanations of the difference between geographic and grid coordinates - how to quote grid references from topographic maps - how to plan a successful trip using topographic maps- using Global Positioning System (GPS) receivers and magnetic compasses with topographic maps - using a topographic map to find your current position and to set a course. This product is the guide/map roamer card combination.

This dataset is about book series. It has 1 row and is filtered where the books is Reading a map. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Excel file contains the filtered data records of the map-reading study of the Research Group on Experimental Cartography at the Eötvös Loránd University (ktk.elte.hu). The data collection started in the autumn of 2015 and lasted until April 2022. The file contains three sheets: demographic_questions; correct_answers; map_reading_database. The first two sheets contain the questions asked, the answer codes, and the correct answers. The third one has 511 records, which is the result of a filtering of the original 805 fills. The filtering excluded the unfinished tests, and the ones with fill time below 2.5 minutes and above 15 minutes.

An introductory idea helping students understand the idea of mapping their locations: their classrooms

These useful see-through map cards are great for reading bearings, distances and grid references on 1:100 000 and 1:250 000 scale maps. The romer can be purchased as part of the Map Reading Guide, …Show full descriptionThese useful see-through map cards are great for reading bearings, distances and grid references on 1:100 000 and 1:250 000 scale maps. The romer can be purchased as part of the Map Reading Guide, or downloaded for reference. A romer can be used for determining the last Easting and last Northing figures for a six-figure grid reference: Place the top right hand corner intersection of the romer lines over the point of interest. Read the numbers from this point to the left to give the final Easting figure, and down to give the final Northing figure. The number required is the last number read, before the grid line on the map crosses the romer.

An official index map of tax maps by municipality for the County of Berks Assessment Department.

Author: M Trepp, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 5, grade 6, grade 7, grade 8Resource type: lessonSubject topic(s): maps, literatureRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Read critically for information
2. Construct a map based on information from a text.
3. Demonstrate accurate maps skills.Summary: Students will construct a sketch map of the place described in the first chapter of the young adult novel. The Island, by Gary Paulsen. Students will use TODALS to finalize their map. Other book selections or short stories could be used, particularly those from English class or popular young adult novels.

Granular and Rock Materials Map of Reading, Vermont. Abstract was not provided, for more information on this resource and accessibility options please see the links provided.

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.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019.

The distribution of the species is based on recent occurrence data (1999-2018 or 2009-2018 by species). These are the natural regions in which at least one observation of the species has been made in the recent period as well as natural regions where the species is highly suspected (i.e. experts) or benefits from older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of meshes 1 x 1 km in which the species was observed. For an explanation of the method of calculation, refer to the explanation sheet of the Natural Regions maps. Natural regions identify territories in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even in one location) makes it possible to strongly assume the existence of other favourable habitats elsewhere in the natural region.

Any comments shall be taken into account: these may be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the map reading instructions as well as PDF cards for more information.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

This dataset is about book subjects. It has 1 row and is filtered where the books is An introduction to map reading for East Africa. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

We propose map reading experiments to quantitatively evaluate the selection of hue ranges for sequential color schemes on choropleth maps. In these experiments, 60 sequential color schemes with six base hues and ten hue ranges were employed as experimental color schemes, and a total of 414 college students were invited to complete identification, comparison, and ranking tasks. Both controlled and real-map experiments were performed, each involving a web-based survey and an eye-tracking experiment. In the controlled experiments, the shapes of the map objects were relatively regular, and attribute data were randomized. In contrast, the shapes were complex in real-map experiments, and real data were employed. Our findings show that widely used color schemes with a hue range of 0º yield poor performance in all tasks; 15º hue ranges yield good performance in the comparison and ranking tasks but poor performance in the identification task. For large hue ranges of 120-360º, participants showed...

The purpose of the Administrative Boundaries map is to help orient readers to the Watershed and provide context for how the watershed(s) relate to administrative boundaries (e.g. cities, townships, counties) in the state.This data has been modified from the original source data to serve a specific business purpose. This data is for cartographic purposes only.

Maps are increasingly read on mobile devices. Mobile maps necessitate specific design considerations to improve readability and user experience. Little research has focused on how to design mobile thematic maps, in contrast to reference maps. Data journalism represents a common way that the public encounters mobile thematic maps. This paper characterizes the design techniques and challenges associated with mobile thematic cartography in the context of data journalism. Through interviews with 18 expert news cartographers, I show that teams of data journalists are increasingly aware of mobile users, but face numerous constraints when designing for these users. They face time constraints, the need to design for both desktop and mobile, and must reach vast general audiences, meaning they often practice simultaneous design over mobile-first design. News cartographers have also reduced their use of interactivity, which reduces complexity related to designing for both desktop and mobile. This work shows that news cartographers solve mobile thematic map design challenges through iterative design processes that draw from years of expertise, not a strict set of guidelines. News cartographers currently design mobile thematic maps based on generalized best practices, but are uncertain what choices do and do not work for their readersMany news cartographers design maps simultaneously for desktop and mobile, rather than prioritizing one over the otherNews cartographers are decreasing their use of interactive maps, given that they expect news readers want to consume information as fast as possibleNews maps are produced under time constraints that can be limiting on creativity and novelty, and without time for user testing News cartographers currently design mobile thematic maps based on generalized best practices, but are uncertain what choices do and do not work for their readers Many news cartographers design maps simultaneously for desktop and mobile, rather than prioritizing one over the other News cartographers are decreasing their use of interactive maps, given that they expect news readers want to consume information as fast as possible News maps are produced under time constraints that can be limiting on creativity and novelty, and without time for user testing

Auxiliary background knowledge and auxiliary map-reading tools.

Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.

The Audio Cartography project investigated the influence of temporal arrangement on the interpretation of information from a simple spatial data set. I designed and implemented three auditory map types (audio types), and evaluated differences in the responses to those audio types.

The three audio types represented simplified raster data (eight rows x eight columns). First, a "sequential" representation read values one at a time from each cell of the raster, following an English reading order, and encoded the data value as loudness of a single fixed-duration and fixed-frequency note. Second, an augmented-sequential ("augmented") representation used the same reading order, but encoded the data value as volume, the row as frequency, and the column as the rate of the notes play (constant total cell duration). Third, a "concurrent" representation used the same encoding as the augmented type, but allowed the notes to overlap in time.

Participants completed a training session in a computer-lab setting, where they were introduced to the audio types and practiced making a comparison between data values at two locations within the display based on what they heard. The training sessions, including associated paperwork, lasted up to one hour. In a second study session, participants listened to the auditory maps and made decisions about the data they represented while the fMRI scanner recorded digital brain images.

The task consisted of listening to an auditory representation of geospatial data ("map"), and then making a decision about the relative values of data at two specified locations. After listening to the map ("listen"), a graphic depicted two locations within a square (white background). Each location was marked with a small square (size: 2x2 grid cells); one square had a black solid outline and transparent black fill, the other had a red dashed outline and transparent red fill. The decision ("response") was made under one of two conditions. Under the active listening condition ("active") the map was played a second time while participants made their decision; in the memory condition ("memory"), a decision was made in relative quiet (general scanner noises and intermittent acquisition noise persisted). During the initial map listening, participants were aware of neither the locations of the response options within the map extent, nor the response conditions under which they would make their decision. Participants could respond any time after the graphic was displayed; once a response was entered, the playback stopped (active response condition only) and the presentation continued to the next trial.

Data was collected in accordance with a protocol approved by the Institutional Review Board at the University of Oregon.

• Additional details about the specific maps used in this are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

• Details of the design process and evaluation are provided in the associated dissertation, which is available from ProQuest and University of Oregon's ScholarsBank.

• Scripts that created the experimental stimuli and automated processing are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

Preparation of fMRI Data

Conversion of the DICOM files produced by the scanner to NiFTi format was performed by MRIConvert (LCNI). Orientation to standard axes was performed and recorded in the NiFTi header (FMRIB, fslreorient2std). The excess slices in the anatomical images that represented tissue in the next were trimmed (FMRIB, robustfov). Participant identity was protected through automated defacing of the anatomical data (FreeSurfer, mri_deface), with additional post-processing to ensure that no brain voxels were erroneously removed from the image (FMRIB, BET; brain mask dilated with three iterations "fslmaths -dilM").

Preparation of Metadata

The dcm2niix tool (Rorden) was used to create draft JSON sidecar files with metadata extracted from the DICOM headers. The draft sidecar file were revised to augment the JSON elements with additional tags (e.g., "Orientation" and "TaskDescription") and to make a more human-friendly version of tag contents (e.g., "InstitutionAddress" and "DepartmentName"). The device serial number was constant throughout the data collection (i.e., all data collection was conducted on the same scanner), and the respective metadata values were replaced with an anonymous identifier: "Scanner1".

Preparation of Behavioral Data

The stimuli consisted of eighteen auditory maps. Spatial data were generated with the rgeos, sp, and spatstat libraries in R; auditory maps were rendered with the Pyo (Belanger) library for Python and prepared for presentation in Audacity. Stimuli were presented using PsychoPy (Peirce, 2007), which produced log files from which event details were extracted. The log files included timestamped entries for stimulus timing and trigger pulses from the scanner.

• Log files are available in "sourcedata/behavioral".
• Extracted event details accompany BOLD images in "sub-NN/func/*events.tsv".
• Three column explanatory variable files are in "derivatives/ev/sub-NN".

References

Audacity® software is copyright © 1999-2018 Audacity Team. Web site: https://audacityteam.org/. The name Audacity® is a registered trademark of Dominic Mazzoni.

FMRIB (Functional Magnetic Resonance Imaging of the Brain). FMRIB Software Library (FSL; fslreorient2std, robustfov, BET). Oxford, v5.0.9, Available: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/

FreeSurfer (mri_deface). Harvard, v1.22, Available: https://surfer.nmr.mgh.harvard.edu/fswiki/AutomatedDefacingTools)

LCNI (Lewis Center for Neuroimaging). MRIConvert (mcverter), v2.1.0 build 440, Available: https://lcni.uoregon.edu/downloads/mriconvert/mriconvert-and-mcverter

Peirce, JW. PsychoPy–psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2):8 – 13, 2007. Software Available: http://www.psychopy.org/

Python software is copyright © 2001-2015 Python Software Foundation. Web site: https://www.python.org

Pyo software is copyright © 2009-2015 Olivier Belanger. Web site: http://ajaxsoundstudio.com/software/pyo/.

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available: https://www.R-project.org/.

rgeos software is copyright © 2016 Bivand and Rundel. Web site: https://CRAN.R-project.org/package=rgeos

Rorden, C. dcm2niix, v1.0.20171215, Available: https://github.com/rordenlab/dcm2niix

spatstat software is copyright © 2016 Baddeley, Rubak, and Turner. Web site: https://CRAN.R-project.org/package=spatstat

sp software is copyright © 2016 Pebesma and Bivand. Web site: https://CRAN.R-project.org/package=sp

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 = ['