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42 results for “road network”
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
Supplementary material 1 from: Conan A, Fleitz J, Garnier L, Le Brishoual M, Handrich Y, Jumeau J (2022) Effectiveness of wire netting fences to prevent animal access to road infrastructures: an experimental study on small mammals and amphibians. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 271-281. https://doi.org/10.3897/natureconservation.47.71472
Supplementary materials and methods
Supplementary material 1 from: Ferreira EM, Valerio F, Medinas D, Fernandes N, Craveiro J, Costa P, Silva JP, Carrapato C, Mira A, Santos SM (2022) Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 155-175. https://doi.org/10.3897/natureconservation.47.72781
Figures S1–S3
Figure 8b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 8b "Point Pattern Edition" features. - Information that is displayed (marks of the point pattern, if available, as defined by the user) when an event is clicked
Figure 5a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 5a Example of use of the SimplifyLinearNetwork function. - A road network introduced as input in which there is an excess of road segments and vertex
Figure 1 from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 1 Workflow that describes all the steps that could be carried out in order to perform a spatial analysis on a point pattern that lies on a linear network. Some of these steps which lead to the final statistical analysis may be skipped but, at least, all of them should be considered. The blocks pointing the steps of the process include some of the R packages that would allow to successfully achieve each of them.
Figure 3b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 3b "Network Edition" example of use (I). - Network resulting from clicking on "Rebuild linear network" in the situation of a
Figure 2a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 2a "Network Edition" features. - Overview of the "Network Edition" section of the SpNetPrep application
Figure 6b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 6b "Network Direction" features. - Manual addition of traffic flow to the network by using the options "Add flow" and "Add long flow"
Figure 3a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 3a "Network Edition" example of use (I). - Use of the "Join vertex" (in green), "Remove edge" (in red) and "Add point" options (in green) in the SpNetPrep application
Figure 8a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 8a "Point Pattern Edition" features. - An example of a point pattern that lies on a road network as it can be visualized in SpNetPrep
Figure 4b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 4b "Network Edition" example of use (II). - Network resulting from clicking on "Rebuild linear network" in the situation of a
Figure 7 from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 7 Example of a linear road network following usual notation for the edges (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} e_{i} \end{equation*} \end{varwidth} \end{document} ) and vertex (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} v_{i} \end{equation*} \end{varwidth} \end{document} ). Arrows represent the direction of traffic flow.
Figure 6a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 6a "Network Direction" features. - A zone of a road network introduced as an input in the "Network Direction" section of the SpNetPrep application
Figure 4a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 4a "Network Edition" example of use (II). - Another use of the "Join vertex" (in green) option of the "Network Edition" section
Figure 5b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 5b Example of use of the SimplifyLinearNetwork function. - Simplified version of the network in a after the application of the SimplifyLinearNetwork function with parameters Angle = 25 and Length = 65
road network inference algorithms
<p>The document includes the Chicago trajectory dataset and several map inference algorithms.</p>
urbisphere_gb-london_UR-3: Transport database derived from UK road networks, OSM, and TfL public transport timetables, for modelling in London, UK
<h2><strong>Files included in this archive</strong></h2> <p><strong>Documentation</strong></p> <ul> <li> urbisphere_London_transport_database.pdf</li> </ul> <p><strong>JSON Database files</strong> (JSON: <a href="https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml">https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml</a> (<em>last accessed: 30/3/2024</em>))</p> <ul> <li>driving_transport.json <ul> <li>Database of driving routes</li> </ul> </li> <li>cycling_transport.json <ul> <li>Database of cycling routes</li> </ul> </li> <li>walking_transport.json <ul> <li>Database of walking routes</li> </ul> </li> <li>public_transport.json <ul> <li>Database of public transport routes</li> </ul> </li> </ul> <p><strong>Python 3.9 Code</strong></p> <ul> <li>London_travel_dictionaries.py <ul> <li>to create databases</li> </ul> </li> <li>assign_speed_limits.py <ul> <li> to assign OSM speed limits to each road in GLA</li> </ul> </li> <li>reduce_sub_services.py <ul> <li> to group transport routes within the TfL timetables</li> </ul> </li> </ul> <h2>Data purpose</h2> <p>This dataset contains transport routes for walking, driving, cycling, and public transport (train, tube, and bus) within the Greater London (GLA), which can be used for simulations of human behaviour and movement, for example using agent-based models (e.g. Capel-Timms et al. 2021, McGrory et al. 2024b).</p> <h3><em>Associated publications</em></h3> <ul> <li>Hertwig et al. 2024b: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863</li> <li>Hertwig et al. 2024c: urbisphere_gb-london_UR-7: Gridded total road lengths by type for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset].. Zenodo. https://doi.org/10.5281/zenodo.10889841</li> <li>McGrory et al. 2024a: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation].. Zenodo. https://doi.org/10.5281/zenodo.10889900</li> </ul> <div> <div> </div> <div> <p> </p> </div> </div>
Figure 2b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521
Figure 2b "Network Edition" features. - Example of a road network uploaded into the application
Dataset for "Deriving map images of generalised mountain roads with generativeadversarial networks."
<p>This is the dataset used in the paper "Deriving map images of generalised mountain roads with generativeadversarial networks.". The data are derived from an extract of the database used to make the topographic maps at the 1:25,000 scale and 1:250,000 map scale at IGN.</p> <p>The base vector data are presented in shapefile_montain_road folder, the vector manually matched data are in shapefile_manually_matched folder; finally constructed images using manually paired data and a reasonable fixe size grid are in roads_images_manually_matched folder. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.