Shapefile of the City’s street network. This shapefile contains the street segments identified in our streets repair projects and Overall Condition Index datasets. This data is displayed on streets.sandiego.gov.

Field Name DescriptionObjectIDFor internal use.FacilityIDThe tracking number for each street segment.InstallDateInstallation date for the street segment.LifeCycleStatusFor internal use.LocationDescriptionLocation of street segment, from cross street to cross street.CommentsComment field for location.EAM_PARENTFor internal use.EAM_PARENTORGFor internal use.GISOBJIDInfor object ID field.EAM_UOMFor internal use.EAM_UOMREFFor internal use.EAM_PRECISIONFor internal use.StreetClassType of street, such as Residential, Minor arterial, Collector, etc.SubZoneFor internal use.LaneMilesLength of segment, in miles.GlobalIDFor internal use.PCIQuality of road condition, as a percent.WidthWidth of street segment, in feet.ShapeFor internal use.created_userFor internal use.created_dateFor internal use.last_edited_userFor internal use.last_edited_dateFor internal use.StreetSaverFor internal use.UPDATE_COUNTFor internal use.StreetSaverIDID value for StreetSaver.LanesNumber of lanes within street segment.Shape.STLength()For internal use.

Road Segmentation Dataset

This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

The dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb0789a0ec8075d9c7abdb0aa9faced59%2FFrame%2012.png?generation=1694606364403023&alt=media" alt="">

DATASETS WITH VEHICLES :

• License Plates - 1 209 438 OCR Plates
• Car Object Detection & Tracking
• Vehicle Images & Segmentation
• Parking Space Detection & Classification Dataset
• Videos around cars

Dataset structure

• images - contains of original images of roads
• masks - includes segmentation masks created for the original images
• annotations.xml - contains coordinates of the polygons, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Сlasses:

• road_surface: surface of the road,
• marking: white and yellow marking on the road,
• road_sign: road signs,
• car: cars on the road,
• background: side of the road and surronding objects

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa74a4214f4dd89a35527ef008abfc151%2Fcarbon.png?generation=1694608637609153&alt=media" alt="">

Roads Segmentation might be made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

keywords: road surface, road scene, off-road, vehicle segmentation dataset, semantic segmentation for self driving cars, self driving cars dataset, semantic segmentation for autonomous driving, car segmentation dataset, car dataset, car images, car parts segmentation, self-driving cars deep learning, cctv, image dataset, image classification, semantic segmentation

The data contains 100 pairs of images, ground truth, and a test set. We have provided satellite/aerial images acquired from Google Maps. We also offer ground-truth images where each pixel is labelled {road, background}. You aim to train a classifier to segment roads in these images, i.e. assign a label {road=1, background=0} to each pixel.

Street Centerline Segments 2005. A single line representing each street in the District, segmented at major blocks to preserve geocoding integrity. They follow the general trend of the street and do not deviate due to parking lanes, turning lanes, etc. and contain address ranges for geocoding. The street GIS database includes only the major road types: street centerline. This layer contains complete theoretical address ranges.

Description of data fields in the Boston street segment data

City of Boston street segments data from the Street Address Management (SAM) system. Updated nightly.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

A road and lane segmentation dataset containing the following classes.

1. Background - [0,0,0]
2. Roads - [128,0,0]
3. Lane mark solid- [0,128,0]
4. Lane mark dashed - [128,128,0]

The dataset contains 299 training samples and 74 validation samples.

Original dataset credit => https://www.kaggle.com/datasets/nublanazqalani/semantic-segmentation-makassaridn-road-dataset

Articles showing usage: 1. Road Segmentation using SegFormer 2. Multi Class Segmentation using Mask2Former

This dataset contains each parking zone and the street segment that it is linked to. The parking zone can go across multiple street segments and also one street segment can have multiple parking zones. This data can be linked to both the sign plate parking zone dataset. The dataset(s) can be joined on parking zone. To create a spatial dataset the street segment can be joined with the road corridor dataset on the street segment id.

A list of Street Segment and Intersection (CNN) changes including new, dropped, realigned, divided and split records.

This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:

Motor Vehicle Collisions – NYC Open Data: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95

Citywide Street Centerline (CSCL) – NYC Open Data: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b

NYC Building Footprints – NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh

Practical Canopy for New York City: https://zenodo.org/record/6547492

New York City Bike Routes – NYC Open Data: https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7

Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): https://www.sidewalkwidths.nyc/

LION Single Line Street Base Map - The NYC Department of City Planning (DCP): https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

NYC Planimetric Database Median – NYC Open Data: https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d

NYC Vision Zero Open Data (including multiple datasets including all the implementations): https://www.nyc.gov/content/visionzero/pages/open-data

NYS Traffic Data - New York State Department of Transportation Open Data: https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb

Smart Location Database - US Environmental Protection Agency: https://www.epa.gov/smartgrowth/smart-location-mapping

Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs

A. SUMMARY Maximum speed limits per street segment for the City of San Francisco. Speed limits are indicated primarily for streets that have speeds greater than 25 MPH, unless the speed limit has been changed from a higher speed or a speed survey has been conducted to enforce the de facto speed limit of 25 MPH. 25 MPH is the de fact speed limit for most residential and commercial streets, and apply to streets on this map denoted by a 0 MPH label. Alleys narrower than 25 feet can have de facto speed limits of 15 MPH. B. METHODOLOGY Speed limit legislation information is taken from MTAB legislation and in some cases directives from engineers in the 5212 classification. Speed limit implementation information is taken from SSD Shops Reports and then parsed via python code. Implementation for speed limits will specify when signs are put in stating the new speed limits, and the work order that the sign installation was spec'd in. C. UPDATE FREQUENCY Updated quarterly or on an as need basis by request

This table contains data that describes the condition of streets maintained by the City of Austin Public Works Department. The street network consisted of about 2525 miles of streets and just over 7863 lane miles of pavement in fiscal year 2019. Note that since many streets consist of multiple lanes, there are about three times as many lane miles as miles of streets. We report based on lane miles, rather than street miles in order to better capture the public's driving experience. Condition data is gathered by a contacted vendor, who drives the streets using a specially equipped vehicle that records the data used to determine street condition. The vendor drives every lane of every street in the city, covering one-third to one-half of the city every year. Street condition is then classified as excellent, good, fair, poor, or failed based on national street engineering standards. This table contains data for the most recent set of assessment data. Year-to-year performance is reported using another table that contains aggregated values calculated using the method described above. Private streets and streets maintained by the Texas Department of Transportation are excluded from reporting and this data set, since maintenance of those roadways is the responsibility of those parties. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/kara-xhcd.

This dataset shows polygon locations of the road segments inventoried by the City of Perth. The road segment data was collected from as constructed drawings and old survey maps. At least 95% of the locational data for the road segment is accurate to within a few meters. Some errors and/or duplicate data may exist. Show full description

Street segment in the City of Repentigny.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Street Segments

Street segments of the road network.attributes:ID - Unique identifierToponymy - Full street nameNorte - Road numberCivic NumberOriginLeft - Civic number that originated the segment on the left side according to the direction of digitalizationNumberCivicOriginRight - Civic number that originated the segment on the right side according to the direction of digitalizationCivic NumberDestinationLeft - Civic number destined for segment on the left side according to the direction of digitalizationCivic NumberDestinationRight - Civic number to the segment on the right side according to the direction of DigitalNameGeneric - Short name of the streetType - Street typeStreet typeStreet typeSegmentStreet - Hierarchical class of the segment in the networkSpeed - Speed limit displayTypesUnique - Indication relating to the presence of a one-way wayMunicipal - Municipal code - Municipal codeHeavy traffic - Indication relating to heavy traffic**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The dataset contains locations and attributes of blocks, created as part of the Master Address Repository (MAR) for the Office of the Chief Technology Officer (OCTO) and participating DC government agencies. The blocks used street centerlines information from DDOT. Blocks include named alleys in DC (such as 'WALTER ALLEY NE'). A block represents a street segment which usually has two bordering streets. The coordinates for the blocks are located at the midpoint of the street segment. More information on the MAR can be found at https://opendata.dc.gov/pages/addressing-in-dc. The data dictionary is available: https://opendata.dc.gov/documents/2a4b3d59aade43188b6d18e3811f4fd3/explore. In the MAR 2, the BlocksPt is called BLOCKS_PT and is primarily based off of street data from DC Department of Transportation's Roads & Highways database, it also features additional useful information such as created date, last edited date, begin date, and more.

The Cityscapes dataset is a large-scale benchmark for semantic understanding of urban street scenes. It provides high-quality images and fine-grained pixel annotations across multiple classes, making it widely used for training and evaluating deep learning models in semantic segmentation, instance segmentation, and object detection tasks.

Key Features:

• Images: High-resolution street scenes from 50 European cities.
• Annotations: Pixel-level labels for 30 classes, grouped into 8 categories (e.g., road, person, car, building, traffic sign).
• Applications: Ideal for autonomous driving, traffic scene understanding, and computer vision research.
• Tasks: Can be used for semantic segmentation, panoptic segmentation, depth estimation, and object recognition.