This dataset provides information about the number of properties, residents, and average property values for R Road cross streets in Minden, NE.

Rapid and accurate extraction of road infrastructure from high-resolution remote sensing satellite imagery is important for traffic planning, construction, and management. In recent years, road extraction methods have developed rapidly, benefiting from the application of data-driven deep learning based models and the various urban road datasets. However, there are still application bottlenecks when directly transferring the current research from urban to rural areas. Specifically, most road datasets are designed for urban areas, and only a small number of rural scenes are included, without complex rural scenes. Due to the huge style differences between urban and rural roads in different geographical areas, it is difficult to apply the current datasets to rural road extraction. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RRoad, is introduced for rural road extraction, which contains 27770 pairs of 1024 × 1024 satellite images with resolution of 0.3m and corresponding road annotations, covering a 2620.71 km2 rural area in central China. In addition, a comprehensive analysis of the performance of the current state-of-the-art deep learning based road extraction methods on the WHU-RRoad dataset is provided, where the experimental results illustrate that the proposed WHU-RRoad dataset is a challenging dataset for large-scale rural road extraction. At the same time, the WHU-RRoad dataset can meet the application requirements of rural road construction and has great application potential.

This dataset provides information about the number of properties, residents, and average property values for R Road cross streets in Funk, NE.

This dataset provides information about the number of properties, residents, and average property values for R Road cross streets in Petersburg, NE.

This dataset provides information about the number of properties, residents, and average property values for R And R Road cross streets in Gretna, NE.

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

This dataset provides information about the number of properties, residents, and average property values for R Road cross streets in Peru, NE.

Timeseries data from 'TETCELA R AT CANADIAN ZINC ROAD' (ca_hydro_10GD002)

This dataset contains averaged road roughness data (measured as NAASRA roughness counts) over different road segments and classified into condition classes. \r \r Data has been made available at two levels of granularity – 1km road segments and 100m road segments. \r \r Please refer to the appropriate supporting resources for information about the fields within each data resource.\r \r Please note: This dataset has been updated data as at EOFY 2021-2022 \r

Global patterns of current and future road infrastructure - Supplementary spatial data

Authors: Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper

Research paper summary: Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Contents: The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-0) and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the paper by Meijer et al (2018). Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.

Regional coding of the other vector datasets in shapefile and ESRI fgdb format:

• Region 1: North America
• Region 2: Central and South America
• Region 3: Africa
• Region 4: Europe
• Region 5: Middle East and Central Asia
• Region 6: South and East Asia
• Region 7: Oceania

Road density raster data:

• Total density, all types combined
• Type 1 density (highways)
• Type 2 density (primary roads)
• Type 3 density (secondary roads)
• Type 4 density (tertiary roads)
• Type 5 density (local roads)

Keyword: global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

Total length of urban lanes on the state-controlled road network in kilometres.\r \r Please note: This is an inactive dataset and is no longer maintained by the Department of Transport and Main Roads. The last update to this dataset was done on the 16/11/2018.\r

Polyline data for location of roads registered with council across the Northern Grampians Shire region.\r \r Field Descriptions\r \r + ref\tNGSC Asset ID\r + name\tAsset Name\r + class\tAsset Class\r + subclass\tAsset Sub Class\r + type\tAsset Type\r + subtype\tAsset Sub Type\r + status\tAsset Status\r + location\tLocation of asset\r + road Address of asset\r + gid\tNGSC Asset GID\r + width\tRoad Width\r + width_traffic\tRoad Width (Sealed)\r + route_use\tHeavy Vehicle Road Use \r \r *Data may not be available for all fields.\r \r Although all due care has been taken to ensure that this data is correct, no warranty is expressed or implied by Northern Grampians Shire Council in its use.

This dataset provides information about the number of properties, residents, and average property values for R Road cross streets in Severy, KS.

The dataset contains data produced by the Finnish Transport Agency’s road weather system on the weather and weather on the roads. Along the roads there are almost 500 roads and weather observing the weather. The largest number of stations are located in the coastal region and southern Finland. Road weather stations provide information every 10-15 minutes from different road surface sensors about the conditions on the road surface and the weather from meteorological sensors. Due to the location of the road meteorological stations, the reliability of weather sensor data and the comparability of data between stations is weaker than the data of the Finnish Meteorological Institute’s weather observation stations, which are located in the most meteorologically representative locations. The main focus of road weather stations is the measurement of the weather and the stations are therefore positioned using different methods than weather observation stations.

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

Smooth travel exposure (percentage of travel undertaken each year on state-controlled urban roads with a roughness level condition of less than 5.33 IRI).\r \r Please note: This is an inactive dataset and is no longer maintained by the Department of Transport and Main Roads. The last update to this dataset was done on the 15/11/2018.

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

Reindeer movement, habitat preference and road permeability model data

Overview: GPS data for wild reindeer were collected within a larger project in Rondane-South and Rondane-North wild reindeer management areas, a mountainous region of central-southern Norway (10 46’ E, 61 38’ N). We used locations collected from five adult female reindeer every three hours between 1 June to 29 September 2012 (N = 973, 960, 871, 971 and 974 locations, respectively). Around 60% of the area is located above tree-line between 1000 and 1500 m, and is dominated by rocks and lichen heath; lower elevations (above 500 m) are characterised by a mix of meadows, grass and willow communities. The area occupied by the reindeer used in this study extends between ca. 400 and 1900 m, and is fragmented by public and private roads (access to the latter is often restricted, so is characterised by lower traffic volumes than the former).

The data we provide can be used in conjunction with the R code in the Supplementary Materials of the published paper to fit the models presented in that paper.

The zip file contains five R data files corresponding to five reindeer. The files are named "data_" followed by one of the ID numbers of the reindeer (11264, 11265, 9397, 7625, 9406). Each R data (*.RData) file contains an R list object called "fitdata" in R, composed of the following data structures:

Definitions: N_s = the number of movement "steps" (sequential telemetry locations) for a reindeer N_a = the number of grid cells in the landscape (all cells falling within 5km of any telemetry point.

(i) fitdata$usehab

A matrix (dimensions: N_s rows, 4 columns) of 'used' habitat types (the habitat value at each telemetry location in the movement path). The four columns are: elevation (km), elevation^2, distance to road (km), distance to road^2. The squared terms are included because the habitat preference model uses quadratic terms to allow for non-linear preference with respect to the two habitat covariates.

(ii) fitdata$availhab

A matrix (dimensions: N_a rows, 4 columns) of 'available' habitat types (the habitat value at each raster grid cell in the spatial domain of the analysis). The four columns are: elevation (km), elevation^2, distance to road (km), distance to road^2.

(iii) fitdata$use.xy

A matrix (dimensions N_s + 1 rows, 2 columns) of the x and y coordinates (km) of the reindeer telemetry locations. This matrix is used to precalculate a large distance matrix ("dm") representing the distances among all use and available points (dimensions: N_s rows, N_a columns). Although this 'dm' matrix is very large, pre-calculating the distance matrix greatly improves the speed of model fitting by eliminating the need to repeatedly re-calculate distances.

(iv) fitdata$stepdst

A vector of length N_s - 1 representing the Euclidean distance (km) between consecutive telemetry locations.

(v) fitdata$avail.xy

A matrix (dimensions N_a rows, 2 columns) of the x and y coordinates (km) of the grid cells forming the 'availability' sample.

(vi) fitdata$use.rdzn

A vector of length N_s containing arbitrary ID numbers corresponding to regions of space that are bounded by a network of roads. This could be conceptualised in GIS terms as forming polygons based on the lines contained in the road network, and assigning an arbitrary unique ID to each of those polygons. Road crossings are indicated by a change in the ID number in this time series. The reason for using this approach to identifying road crossings is computational efficiency, and to avoid the assumption that the straight-line connected two consecutive telemetry locations can be used to calculate road crossings. For example, if this straight line 'clips' a bend in a road then that would indicate 2 crossings, while the animal may in fact have remained in the same region without ever crossing a road.

(vii) fitdata$avail.rdzn

A vector of length N_a containing arbitrary ID numbers corresponding to regions of space that are bounded by a network of roads (see vi) for each grid cell in the availability sample.

Telemetry and habitat data for 5 reindeer over 1 summer; R data objects.

Special Use roads are public roads administered by Local Governments, which have a separate use in addition to their typical Road Hierarchy function. Categories of Special Use roads include Aboriginal Access Roads, Industrial Roads and Laneways. Aboriginal Access Roads typically are the main access roads to Aboriginal communities; Industrial Roads are found in designated industrial areas, and Laneways are typically short narrow roads providing access to residential areas. Data DictionarySUSE - Special Use Table Special Use name type alias sqlType length Description

ABORIGINAL ACCESS

Aboriginal Access Road

A Local Government road that is the main access road to a remote Aboriginal community.

INDUSTRIAL

Industrial Road

A Local Government road that carries a greater proportion of heavy vehicles, typically connecting to or located within a designated industrial area

LANEWAY

Laneway

A Local Government road or carriageway that typically accesses the rear or side of lots for residents or service vehicles. Laneways are narrower and carry less traffic than the surrounding road network.

name type alias sqlType length Description

OBJECTID esriFieldTypeOID OBJECTID sqlTypeOther 4 Unique record identifier. Default ESRI field.

ROAD esriFieldTypeString Road sqlTypeOther 30 Main Roads Road Number

ROAD_NAME esriFieldTypeString Road Name sqlTypeOther 240 Name given to road by Main Roads

COMMON_USAGE_NAME esriFieldTypeString Common Usage Name sqlTypeOther 240 Name of the road (approved by Landgate)

START_SLK esriFieldTypeDouble Start SLK sqlTypeDouble 8 Minimum SLK (chainage/measure) of the road

END_SLK esriFieldTypeDouble End SLK sqlTypeDouble 8 Maximum SLK (chainage/measure) of the road

CWY esriFieldTypeString CWY sqlTypeOther 6 Carriageway (L - Left, R - Right, S - Single)

START_TRUE_DIST esriFieldTypeDouble Start True Dist sqlTypeDouble 8 Minimum geometric measure of the road

END_TRUE_DIST esriFieldTypeDouble End True Dist sqlTypeDouble 8 Maximum geometric measure of the road

NETWORK_TYPE esriFieldTypeString Network Type sqlTypeOther 30 Type of network (examples include State Road, Local Road etc)

RA_NO esriFieldTypeString Main Roads Responsibility Area No. sqlTypeOther 2 Main Roads responsibility area (or region) number

RA_NAME esriFieldTypeString Main Roads Responsibility Area Name sqlTypeOther 30 Main Roads responsibility area (or region) name

LG_NO esriFieldTypeString Local Government No. sqlTypeOther 3 Local government area number

LG_NAME esriFieldTypeString Local Government Name sqlTypeOther 30 Local government area name

SPECIAL_USE esriFieldTypeString Special Use sqlTypeOther 20 Special use in addition to the Road Hierarchy of Local Government roads (examples Aboriginal Access, Industrial, Laneway)

ROUTE_NE_ID esriFieldTypeInteger Route ID sqlTypeInteger 4 Route identifier

GEOLOC.STLength esriFieldTypeDouble GEOLOC.STLength() sqlTypeDouble

Geometry Location