The dataset contains 62,058 high quality Google Street View images. The images cover the downtown and neighboring areas of Pittsburgh, PA; Orlando, FL and partially Manhattan, NY. Accurate GPS coordinates of the images and their compass direction are provided as well. For each Street View placemark (i.e. each spot on one street), the 360° spherical view is broken down into 4 side views and 1 upward view. There is one additional image per placemark which shows some overlaid markers, such as the address, name of streets, etc. ### Citation: Please cite the following paper for which this data was collected (partially): Image Geo-localization based on Multiple Nearest Neighbor Feature Matching using Generalized Graphs. Amir Roshan Zamir and Mubarak Shah. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014.

Dataset Card for panoramic street view images (v.0.0.2)

  Dataset Summary

The random streetview images dataset are labeled, panoramic images scraped from randomstreetview.com. Each image shows a location accessible by Google Streetview that has been roughly combined to provide ~360 degree view of a single location. The dataset was designed with the intent to geolocate an image purely based on its visual content.

  Supported Tasks and Leaderboards

None as of now!… See the full description on the dataset page: https://huggingface.co/datasets/stochastic/random_streetview_images_pano_v0.0.2.

Street View is a large-scale dataset of 3D scenes, consisting of millions of images taken from street-level cameras.

A large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information.

Google street view from line to point layer. Updated as necessary.

Dataset Name: MPOINSLI Mapillary POI-Neighborhood Street-Level Images

This is a repository of Mapillary street-view images of New York City that include any portion of POIs in their field of view. The repository is the outcome of a paper, the abstract of which is provided below. Please use the below citatin for using this dataset:

Citation:

N. Zarbakhsh and G. McArdle, "Points-of-Interest from Mapillary Street-level Imagery: A Dataset For Neighborhood Analytics," 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), Anaheim, CA, USA, 2023, pp. 154-161, doi: 10.1109/ICDEW58674.2023.00030.

Abstract:

The Sustainable Development Goals of the United Nations promote sustainable urban development to make cities more economically and socially liveable. Points of Interest (POIs) such as commercial properties and healthcare facilities are significant markers for these goals. Street-view images are becoming increasingly important for capturing cities' streetscapes. Existing studies provide city-level images, while there are few studies that provide images in the vicinity of certain POIs. Therefore, this paper develops a framework for filtering images so that a portion of a given POI is visible in their field of view (FOV). We contribute with Mapillary POI-Neighborhood Street-Level Images (MPOINSLI) dataset, a large street-view image of POIs and their neighborhood in New York City. First, all the images within a 35-meter radius of certain POIs are filtered. Then, the intersection technique is utilized to determine if the cameras' FOV triangular polygons intersect the POIs' polygons. Using 11,126 POIs from SafeGraph's Geometry and Place datasets in conjunction with 875,592 Mapillary images, we demonstrate the effectiveness of our approach. MPOINSLI contains 167,743 Mapillary street-view images of 6,732 unique POIs, defined by the standard identifiers (Placekeys) which are further classified into 23 general functionalities categories (top-categories) and 67 more specific categories (sub-categories) of the POIs. MPOINSLI provides an open-source repository that contains metadata such as raw and post-processed camera-related parameters, the Harvesian distance between the camera and the POI's coordinates, and the intersection area. MPOINSLI could provide promising future applications for both smart cities and computer vision, including scene recognition across POI neighborhoods and fine-grained land-use classification.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

The data presented in this article is related to the research article entitled "Urban design using generative adversarial networks: optimising citizen health and wellbeing" (Wijnands et al 2018). The data consists of Google Street View (Google Maps, 2017) imagery (4,473,991 images, 8-bit JPEG at 256x256 resolution) from four headings (0, 90, 180, and 270 degrees) at 1,118,534 locations in the greater metropolitan area of Melbourne, Australia. Locations were determined using the nodes of the vector lines in the PSMA Street Network dataset (PSMA 2018) and data was post-processed by removing indoor images. Please cite this paper if you use the dataset.

The data is broken up into four archives, 000.zip, 090.zip, 180.zip, and 270.zip, containing the imagery from each compass heading. A csv file (contained in MelbourneStreetViewImagesData.zip) provides a mapping between the filenames, location names, direction, latitude, and longitude.

Street View House Numbers (SVHN) is a digit classification benchmark dataset that contains 600,000 32×32 RGB images of printed digits (from 0 to 9) cropped from pictures of house number plates. The cropped images are centered in the digit of interest, but nearby digits and other distractors are kept in the image. SVHN has three sets: training, testing sets and an extra set with 530,000 images that are less difficult and can be used for helping with the training process.

The authoritative City of Sioux Falls street map(s).

Little research in education has focused on school neighborhoods. We employ a novel systematic social observation tool – the internet-based School Neighborhood Assessment Protocol (iSNAP) – within Google Street View to quantify the physical characteristics of 291 preschool communities in nine US cities. We find low to moderate correlations (r = -.03 to -.57) between iSNAP subscales and Census tract poverty, density, and crime, suggesting that the characteristics captured by the iSNAP are related to yet ultimately distinct from existing neighborhood structural measures. We find few positive associations between iSNAP community characteristics and 1,230 low-income preschoolers’ end-of-year outcomes. Specifically, resources for outdoor play (e.g., playgrounds, open fields) on school grounds predicted stronger child self-regulation skills, whereas global ratings of safety and care for both the school grounds and surrounding neighborhood predicted stronger approaches to learning skills. Indicators of physical order were not associated with child outcomes.

This statistic shows the results of a survey on the usage of the internet to look at cities and streets (e.g. via Google Street View) online in Germany from 2013 to 2015. In 2013, there were about 4.41 million people among the German-speaking population aged 14 years and older who frequently viewed cities and streets online.

This dataset is used for urban village classification. The data source is Google Earth level-17 high-resolution remote sensing imagery (2.15m) and Tencent streetview data. The dataset contains 856 and 1714 image samples corresponding to the two categories of urban villages and non-urban villages, respectively, which are sampled in Beijing, Tianjin and Shijiazhuang. After data preprocessing, per sample contains one remote sensing image and four corresponding streetview images, and all image sizes are 224 × 224 × 3. The dataset is divided into training and test set using the ratio 7 : 3, and then the training and validation set are divided from the training set using the ratio 8 : 2.

This vector tile layer is designed to support exporting small volumes of basemap tiles for offline use. The content of this layer is equivalent to World Street Map (with Relief). This layer includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries, designed for use with shaded relief for added context. See World Street Map (with Relief) for more details.Use this MapThis vector tile service supporting this layer will enable you to export a small number of tiles in a single request. This layer is not intended to be used to display live map tiles for use in a web map or web mapping application. To display map tiles, please use World Street Map (with Relief).Service Information for DevelopersTo export tiles for World Street Map (with Relief- for Export), you must use the instance of the World_Basemap_Export_v2 service hosted on basemaps.arcgis.com referenced by this layer (see URL in Contents below), which has the Export Tiles operation enabled. This layer is optimized to minimize the size of the download for offline use. Due to this optimization, there are small differences between this layer and the display optimized World_Basemap_v2 service. This layer is intended to support export of basemap tiles for offline use in ArcGIS applications and other applications built with an ArcGIS Runtime SDK.

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points). All of the Digital City Map (DCM) datasets are featured on the Streets App All previously released versions of this data are available at BYTES of the BIG APPLE- Archive Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

Important Note: This item is in mature support as of July 2021. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.World Street Map includes highways, major roads, minor roads, one-way arrow indicators, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries, overlaid on shaded relief for added context.This basemap is compiled from a variety of authoritative sources from several data providers, including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), U.S. National Park Service (NPS), Food and Agriculture Organization of the United Nations (FAO), Department of Natural Resources Canada (NRCAN), HERE, and Esri. Data for select areas is sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view. Additionally, data for the World Street Map is provided by the GIS community through the Community Maps Program. For details on data sources contributed by the GIS community in this map, view the list of Contributors for the World Street Map.CoverageThe map provides coverage for the world down to ~1:72k and street-level data down to ~1:4k across the United States; most of Canada; Japan; Europe; much of Russia; Australia and New Zealand; India; most of the Middle East; Pacific Island nations; South America; Central America; and Africa. Coverage in select urban areas is provided down to ~1:1k.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer in a web map, see this Streets basemap.

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

This vector tile layer presents the World Street Map (with Relief - WGS84) style (World Edition) and provides a basemap for the world, symbolized with a classic Esri street map style. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Additionally, this layer is designed for use with World Hillshade (WGS84). This vector tile layer provides unique capabilities for customization and high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps (WGS84) are updated quarterly.This layer is used in the Streets (with Relief - WGS84) web map included in ArcGIS Living Atlas of the World.Check out other WGS84 basemaps in the World Basemaps (WGS84) group. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.Precise Tile RegistrationThe map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

Additional file 1. Built environment predictors of health-related behaviors and outcomes, with full regression results for demographic covariates.