1.     INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). 2.     FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023: Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by Remote Sensing. Correspondence regarding these data can be directed to: Sean Sloan Department of Geography, Vancouver Island University, Nanaimo, B.C, Canada sean.sloan@viu.ca;  ..., 1.     INPUT 200 SATELLITE IMAGES
The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limi..., , # Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
https://doi.org/10.5061/dryad.bvq83bkg7
1. INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).   2. FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023:  Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by...
A reference for Weld County roads which have alternate names in municipal areas. For example, Weld County Road 64 becomes O Street in the City of Greeley. For each Weld County road, the table provides the name of the road in unincorporated Weld County, the identity of the municipal area it crosses, the road’s orientation, and the name of the road in the municipal area.
Reference posts (often called Mile Post or Mile Markers) are green numbered signs along the side of a highway roughly a mile apart for traveler reference.
Check other metadata records in this package for more information on Highway Reference Post Information.
Link to ESRI Feature Service:
Highway Reference Posts in Minnesota: Highway Reference Posts
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
The database contains the road sections of the Regional Reference System (RBS). Road segments are polylines that are digitized in the RBS based on roads specified by civil engineering departments. For every official street name or place there is at least one street segment in the RBS.
This Functional Classification dataset was exported from Caltrans Linear Reference System (LRS) on July 3rd, 2024. The LRS serves as the framework upon which the Highway Performance Monitoring System (HPMS) and other business data are managed.
Positions of the reference points along the Flemish motorways and regional roads with the corresponding inscriptions. These reference points serve to determine a location on a Flemish motorway or regional road in a simple manner. These are approximately 100 m apart, but a deviation of 20 meters is allowed. We measure the object or location with a distance to the reference point, always with a road number (Ident8). Ex. the traffic sign is on the right on the N0010001 ref point 16.2 +15m.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The subset contains selected standard road condition data from the LiRA project. The subset contains standard road condition measurements from vehicles operated by the Danish Road Directorate (DRD). The surveying included: (i) P79 Profilometer – a van equipped with a beam hosting 25 point lasers that measure longitudinal and transverse profiles; (ii) ARAN9000 – a multi-functional road scanning vehicle that quantifies road defects and distresses using cameras and a Laser Cracking Measurement System (LCMS); and (iii) VIAFRIK – a skid resistance device complying with CEN/TS 15901-5 standard. The reference data (.csv) is stored in a flat format; each column represents a road condition parameter, GPS information or timestamp, while each row represents a new record in time or space.
This item is part of the collection "Live Road Assessment Custom Dataset (LiRA-CD)", https://doi.org/10.11583/DTU.c.6659909
Arctic Basemap using NSIDC Polar Stereographic North projection
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Caltrans All Roads Linear Referencing Services (LRS) dataset provides the base geometry for federally required Highway Performance Monitoring System (HPMS) business data, functionally classified roads for the California Roads System (CRS) (a requirement for federal funding of local agency projects), and the State Highway Network (SHN), which supports a wide range of internal Caltrans business needs. Description The Federal Highway Administration (FHWA) requires all state DOT's to develop and submit a Linear Referencing System (LRS) network for all public roads in their respective states known as the All Roads Network of Linear Referenced Data (ARNOLD). This ARNOLD requirement is an integral part of each state’s federally mandated Highway Performance Monitoring System (HPMS) annual submittal. To meet the ARNOLD requirement, the Division of Research, Innovation and System Information (DRISI) has developed a representation of all roads in California using a combination of the Census Bureau’s Topologically Integrated Geographic Encoding and Reference (TIGER) files and previously developed line work representing the State Highway System. This data is published publicly.
A reference for Weld County roads which cross into neighboring counties. For example, Weld County Road 76 becomes County Road 16 in Logan County. For each Weld County road, the table provides the name of the road in Weld County, the identity of the neighboring county, and the name of the road in the neighboring county.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The State Maintained Roads dataset is derived from the TopoRoad dataset that is jointly maintained by DEWNR & DPTI. The Common Road Referencing System (CRRS) assigns a unique number to all roads of interest to Department for Planning,Transport and Infrastructure (DPTI) (currently or previously maintained) and defines the start, direction and end of the road for linear referencing (i.e. driven distance) purposes. The system defines reference points with known linear distances (known as road running distance). DPTI asset information is recorded in databases referenced using CRRS standards. This data set is for the Roads Maintained by the State or Federal Government.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
Geographic reference (network arcs) for traffic data from permanent sensors.
This dataset contains the reference of the arrangement of the arcs of the specified network, support data from permanent road traffic sensors, projection in EPSG:2154.
The join with the traffic data must be done using the attribute iu_ac.
Linked to the following dataset:Road counting - Traffic data from permanent sensors
Long-urban road reference of the Inter-departmental Direction des Routes du Massif Central. All of the structuring national road network (national roads and unlicensed motorways) managed by the DIR Massif Central.
A road feature is a link set which represents a collection of road link features that share the same name (for example, Bilston Road) or classification number (for example, A41), used primarily by motorised vehicles. A road will reference the complete collection of road link features irrespective of which authority boundary it falls within. The feature type will include Motorways, A Roads, B Roads, and Named Thoroughfares (roads). The link set may not be contiguous across junctions or where a classified road consists of separate sections, which may be separated by some considerable distance. In addition, the same road link feature may be referenced by multiple road features.
11/22/2024 – The 4 lanes of US 85 from south of the Little Missouri to south of Watford City was added, US 2 was realigned near RP 313, about 2 miles east of Petersburg. There have also been numerous intersections and ramps "trued up" for preparation of MIRE intersections. 1/27/2022 – Business 94 at Dickinson Interchange 64 – The NE & SE ramps were realigned due to 2021 construction. The 2 ramps and 94B were adjusted to a single intersection. Hwy 20 between reference points 87 and 90 was realigned due to a grade raise several years ago.3/1/21 - Realigned the following highways: Hwy 1804 - Mileposts 300–301, 303–305, and 317-317.979, Hwy 85 - Mileposts 123-130, Hwy 23A – Milepost 911 and Hwy 35 - Mileposts 0–12/3/2020 – Minot Bypass - Added the southbound route to Hwy 83B (Route ID = 10). This includes realignment of the south bound ramp at reference point 921.5. Also realigned two ramps at the I94 and University Drive interchange in Fargo.12/4/19 - NewTown Bypass – 1.3714 miles was added to Route ID 297. A new reference point was created at the intersection of Route ID 297 and Route ID 205 (Hwy 23) at 48.684 and at 925.629 (Hwy 23B). Reference points were also created at 926 and 927 on Route ID 297 (Hwy23B). The ramps at the Sheyenne Interchange in West Fargo were updated. The Route ID = 169. The 5 existing ramps were realigned and 3 other ramps were created.9/13/18 - Route ID 4/253 - US 2 Business in Williston, alignment change as it intersects US 2. Route ID 67 - 32nd Avenue interchange on I-29, addition of exit ramp for I-29 Southbound traffic. Route ID 205 - alignment change at the intersection of ND 23 and County Roads 55 and 103/21/17 - Route ID 68 Interchange 64.252 on I-29 in Fargo. This interchange is 13th Avenue and I-29 one leg of the ramp was realigned.1/25/17 - started to maintain roads in Esri's Road and Highways. The shapes now contain measures in miles along with the associated linear referencing/roads and highways fields. Changes also include adding ramps and mainline designations in RTE_SUFFIX field.9/22/16 - added Killdeer Bypass 1. ND 22 North Route ID = 272, Created a new alignment for ND 22 west of Killdeer that begins with intersection of ND 200 and travels northeasterly to a junction with ND 22B north of Killdeer. New reference points were created on the new alignment as follows: 105.710 intersection of ND 22 and ND 200, 106, 107, 108, 109, 109.518 intersection of ND 22 and ND 22B north of Killdeer.2. ND 22 Business (Killdeer) Route ID = 302, The existing ND 22 through Killdeer becomes ND 22 Business due to the completion of a bypass route constructed west of Killdeer. New reference points were created as follows: 940.466 this junction of ND 22, ND 200 and ND 22B. Current ND 22 reference points through Killdeer remain in place but the number changes as follows: 105.000 = 941.000, 106.000 = 942.000, 107.000 = 943.000, 108.000 = 944.000, 109.000 = 945.000. Reference point 945.518 intersection of ND 22B and ND 22 north of Killdeer was also created.3. ND 200 East Route ID = 200, A new reference point 93.247 was created for the intersection of ND 22 and ND 200 west of Killdeer.9/6/16 by bb - added Dickinson Bypass and associated Reference Points.3/1/16 by bb - realigned US 85 North Route ID = 261 to match 2015 NAIP. Added Ramps on 94 at Mile Marker 56.668.10/20/2015 by bb - 1. US 85 North Route ID = 261, Created new alignment for US 85 North that follows the permanent NW Bypass at Williston. New reference points were created as follows: 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 196.705 this is the junction of US 85 and US 2 north of Williston.2. US 85 South Route ID = 300, Created new alignment for US 85 South that completes the four lane project between Alexander and Williston. New reference points were created as follows: 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, and 183.743. This is the junction with the US 2 west of Williston. Created new alignment for US 85 that follows the permanent NW Bypass at Williston. New Reference points created as follows: 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 196.705 this is the junction of US 85 and US 2 north of Williston.3.US 2 East Route ID = 253, alignment change north of Williston at junction of US 85, also created new reference point 25.674 at the intersection of US 85 on north end of NW Permanent Bypass.4. US 2 West Route ID = 254, alignment change north of Williston at junction of US 85, also created new reference point 25.674 at the intersection of US 85 on north end of NW Permanent Bypass.5. ND 3 Route ID = 263, Realignment and grade raise south of Junction of ND 200 west of Hurdsfield has been corrected.5/14/15 by bb - US 281 slip ramp at Church’s Ferry has been removed during a reconstruction project. This segment of roadway was designated as Route ID 25, Interchange ID 175.379, reference points 3.000 and 4.500.2/6/15 by bb - An extension of ND 46 has been added to the State Highway system. The segment of roadway east of I-29 was removed from the State Highway system in 1977. As of May 2011, a document showing a maintenance agreement with a county, as required by State Law to transfer responsibility, has not been found. The Fargo District has been doing basic maintenance on this segment. The segment of road that begins at I-29 and extends to the east until the intersection CMC 0957 is added to the State Highway system. The addition will result in an increase of 0.4 miles. This addition has also created a new reference point 120.823 which is at the end of the highway, reference point 120.318 is moved to the center of the structure on I-29.11/18/14 by bb - added New Town Bypass11/3/14 by Gerald - extended US85 Southbound to Mile Marker 172US 85 South Route ID = 300, Created new alignment for US 85 South that follows the Watford City SW Bypass and the West Bypass at Alexander as well as the new alignment for the 4-lane project that continues to reference point 172.000 which is north of McKenzie County Highway 16. New reference points were created as follows: 139.082 beginning of highway south of Watford City, 140.831 junction with the US 85 Business in Watford City, 145.659 junction of US Business west of Watford City, 160.505 junction with US 85 Business south of Alexander, 163.506 junction of US 85 north of Alexander. Created all whole number reference points between 139.082 and reference point 172.000.10/29/14 by Gerald 1. ND 23 Business Route ID = 297, Changed existing ND 23 in Watford City from the previous Junction of US 85 to the Junction of ND 1806 to ND 23B also changed the reference points 0.000 = 900.000, 1.000 = 901.000, 1.350 = 901.350, 2.000 = 902.000, 3.000 = 903.000, 3.353 = 903.3532. ND 23 East Route ID = 205, Watford City SE Bypass from the previous Junction of US 85 continuing northeasterly to the current alignment of ND 23. New reference point 0.533 was created for the beginning of the route as well as reference point 3.701 which is the Junction with ND 1806 extension.3. ND 23 West Route ID = 299, Watford City SE Bypass from the previous Junction of US 85 continuing northeasterly to the current alignment of ND 23. New reference point 0.533 was created for the beginning of the route as well as reference point 3.751 which is the termination point of this route.4. ND 1806 Route ID = 271, Created an extension of ND 1806 from the Junction of ND 23 B north of Watford City to the Junction of the ND 23 along the new alignment. New reference point 311.577 was created for the intersection.5. US 85 North Route ID = 261, Created new alignment for US 85 North that follows the Watford City SW Bypass and the West Bypass at Alexander as well as the new alignment for the 4-lane project that continues to the Junction of US 2 at Williston. New reference points were created as follows: 140.831 junction with the US 85 Business in Watford City, 145.659 junction of US Business west of Watford City, 160.505 junction with US 85 Business south of Alexander, 163.506 junction of US 85 north of Alexander.6. US 85 South Route ID = 300, Created new alignment for US 85 South that follows the Watford City SW Bypass and the West Bypass at Alexander as well as the new alignment for the 4-lane project that continues to the Junction of ND 200 North of Alexander. New reference points were created as follows: 140.831 junction with the US 85 Business in Watford City, 145.659 junction of US Business west of Watford City, 160.505 junction with US 85 Business south of Alexander, 163.506 junction of US 85 north of Alexander. Created a new reference point 139.082 which is the beginning point for US 85 South along with all reference points between this point and the Junction of ND 200 North of Alexander.7. US 85 Business North Route ID = 298, The existing route US 85 through Watford City and Alexander becomes US 85 Business due to the completion of the Bypass routes. New reference points that were created are: 950.000 intersection with US 85, 950.555 intersection with ND 23, 951.000, 952.000, 952.486 intersection with ND 23 A, 952.707 intersection with ND 23 B, 953.000, 954.000, 955.000, 956.000, 956.233 intersection with US 85 west of Watford City, 970.079 intersection with US 85 south of Alexander, 971.000, 972.000, 973.000 intersection with US 85 north of Alexander.8. Several small alignment changes occurred due to construction projects: ND 8 Junction ND 50 to Bowbells. ND 22 slide repair through the badlands. ND 57 and ND 20 South of Devils Lake. ND 31 bridge replacement 13 miles north of South Dakota Border. ND 23A in Watford City, this highway had the wrong designation as ND 23B, this change corrected this error and is now correctly designated as ND 23A.9. Junction of ND 1804 and ND 58 near Trenton, the intersection of these two highways was realigned.10. US 2 alignment in Williston near 11th street to near 9th Ave NW.Changed all RTE_SIN codes to either I, U, or S.2/15/13 - changes made
Geographical layout of the departmental roads of the Loiret. The data contains the route identifier under different syntaxes. Itinerary measures are included. They make it possible to determine the direction of the road and the distance in meters of each point from the origin. This layer can therefore be used for linear route event referencing. Collection context This reference allows you to know and identify the departmental road network. It is the medium that allows to make cartographic representations of road data located on the Road + PR + abscisse. Collection method The sources used for building and updating are the IGN TOPO® BD, the mapping plans for new developments or GPS surveys. Attributes | Champ | Alias | Type | | — | — | | — | | ‘ROUTE’ | Road name (short name) | ‘char’ | | ‘GESTION’ | Name of road manager | ‘char’ | ‘GESTION’ | ‘OBJECTID_1’ | ‘integer’ | | ‘SHAPE.LEN’ | ‘double’ | | ‘IDROUTE’ | Road name (long name) | ‘char’ | For more information, see the metadata on the Isogeo catalog.
GTRN_PUB_ROADS_ARC: Publication transportation dataset showing both BLM inventoried and non-inventoried roads in Oregon & Washington. This data does not include highways. For highway data see the citation in the Cross Reference Section.
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
Highway Reference Point is a visibly recognizable feature used to describe and identify a point on the Highway (i.e., a reference point abstracted on the Highway and defined by a physical landmark such as an intersection). HRP Landmarks are used in order to provide reference points relating to inventory item data
This feature class consists of approximately 195,000 features representing over 70,000 miles of Interstate, Primary, Secondary and Urban roads throughout the State of Virginia. The Linear Referencing System is based on the Virginia Department of Transportation's Source System of Record for road inventory, Roadway Inventory Management System (RIMS). Geometry and Attribution: The Linear Referencing System (LRS) data contained within this feature class provides dissolved route segmentation (i.e. routes are not segmented when they intersect other routes), thus rendering one table record per route. Multi-part geometry is created where routes are noncontiguous (e.g. a valid physical gap exists because another route is the master). The feature class only depicts master routes, those are routes built in the prime direction and on divided roadways where the non-prime direction is the master. Each road centerline record has a master route record assigned. Measures: The linear reference is based on Official State Mileage (OSM) as derived from reference points at Roadway Inventory Management System (RIMS) roadway intersections (i.e. nodes/junctions). Purpose: This linear referenced data layer represents roadways that are maintained by the Virginia Department of Transportation and provides the underlying spatially enabled geometric network to which all "events" (e.g. potholes, pavement type, vehicle accidents, traffic counts, culverts, etc...) can be located. Note: The overlap and non-prime measures are for reference only and have not been fully validated for accuracy or completeness.
1.     INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). 2.     FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023: Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by Remote Sensing. Correspondence regarding these data can be directed to: Sean Sloan Department of Geography, Vancouver Island University, Nanaimo, B.C, Canada sean.sloan@viu.ca;  ..., 1.     INPUT 200 SATELLITE IMAGES
The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limi..., , # Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
https://doi.org/10.5061/dryad.bvq83bkg7
1. INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).   2. FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023:  Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by...