Content This dataset is composed of 10k images from Google Street Map.

The coords.csv file holds latitude and longitude information for all 10k images. The images themselves have a size of 640x640. All the coordinates come directly from google street map so they are 100% accurate.

Contribute The script to get those image is available as free software a https://github.com/paulchambaz/geotrouvetout.

License This dataset is licensed under the GPLv3 license, feel free to use it however you want.

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.

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.

The Street View House Numbers (SVHN) dataset is a dataset of 604,300 images of house numbers taken from Google Street View. The dataset is split into a training set of 73,257 images, a test set of 26,032 images, and a validation set of 50,113 images. The images in the dataset are all 32 x 32 pixels in size and are in grayscale. The dataset is used to train and evaluate machine learning models for the task of digit recognition.

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.

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

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

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.

In Global Street View Camera Market ,Google Street View is a powerful tool that provides users with an interactive experience of a location. It uses a combination of technologies, including Google Maps, Street View, and satellite imagery to offer a unique perspective of the world around us.

This large vector dataset contains high resolution air pollution mapping of NO, NO2, O3, CH4, CO2, BC, PN2.5, and UFP concentrations in California between June 2015 and June 2019. The dataset consists of measurements collected using four Google Street View vehicles equipped with the Aclima mobile measurement and data integration platform from 2015-05-28 to 2019-06-07. Not all four cars were actively mapping over the entire time frame. Note that there may be gaps in the data when an individual car was not mapping due to operational, mechanical, or system difficulties. Dates of operation for each of the four cars: Car A: 2016-05-03 - 2019-04-30 Car B: 2016-05-03 - 2018-06-08 Car C: 2015-05-28 - 2019-06-07 Car D: 2015-06-24 - 2018-11-05 Data was collected in several geographic regions of California including the San Francisco Bay Area, Los Angeles, and the northern San Joaquin Valley. Mapping occurred in targeted neighborhoods or cities within these regions. The data set contains a table titled "California_Unified_2015_2019" which consists of the concentration of the pollutants Ozone (O3), Nitrogen Dioxide (NO2), Nitrogen Monoxide (NO), Methane (CH4), Carbon Dioxide (CO2), Black Carbon (BC), particle number less than 2.5 micrometers in size (PN2.5), and Ultrafine Particles (UFP) measured using four Google Street View cars equipped with fast time-response, laboratory-grade instruments. The data was collected at 1-Hz time resolution from 20150528 to 20190607 for roads in three regions of California - the San Francisco Bay area, Los Angeles, and the northern San Joaquin Valley. Specific areas mapped varied by region based on desired spatial data coverage and science questions. Each data point is geolocated with latitude and longitude as well as the identity and speed of the car. For details including methodologies, standards, data providers, metadata field definitions and descriptions, refer to the metadata.

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

Annotations of critical electrical infrastructure from 2,221 Google Street View (GSV) images are provided here. Wires and poles observed in the scene were annotated by drawing lines over the wires and polygons encapsulating the poles, and creating a binary mask. The collected GSV images and corresponding binary masks are provided here and are suitable for semantic segmentation research. For ease of use for different research applications, binary masks are divided into images containing power wires only, power poles only, or both.

This dataset contains the latest Open Street Map (OSM) objects for Greece, including the following elements: 1) Buildings, 2) Land Use Information, 3) Natural Objects, 4) Places, 5) Places of Faith and Worship, 6) Points of Interest, 7) Railway Networks, 8) Road Networks, 9) Points of Traffic Interest, 10) Mixed Transportation Hubs, 11) Water Bodies, and 12) Waterways.

Information and data were collected from: www.geofabrik.de

Το εν λόγω σύνολο δεδομένων περιλαμβάνει τα νεότερα Open Street Map (OSM) αντικείμενα για την Ελλάδα. Περιλαμβάνει τα ακόλουθα αντικείμενα: 1) Κτίρια, 2) Χρήσεις Γης, 3) Φυσικά Αντικείμενα, 4) Τοποθεσίες, 5) Χώροι Λατρείας, 6) Σημεία Ενδιαφέροντος, 7) Σιδηροδρομικά Δίκτυα, 8) Οδικά Δίκτυα, 9) Σημεία Συγκοινωνιακού Ενδιαφέροντος, 10) Συγκοινωνιακοί Κόμβοι, 11) Υδάτινοι Φορείς και 12) Υδάτινοι Δίαυλοι.

Οι επιμέρους πληροφορίες και δεδομένα συλλέχθηκαν από το: www.geofabrik.de

Here are a few use cases for this project:

1. Retail Analysis and Mapping: Using the "Google Street View Store Dataset (With Rotation)", businesses and researchers can analyze the distribution of different store types, identify areas with a high concentration of specific stores, and visualize the layout of retail landscapes within cities or regions.

2. Store Accessibility Assessment: City planners and disability advocacy organizations can use the dataset to evaluate the accessibility of stores and shopping areas for individuals with disabilities, considering factors such as store locations, entrances, and nearby parking facilities.

3. Competitor Analysis and Strategic Planning: Companies can use the dataset to identify the locations of competitors' stores and assess their market presence in specific areas. This can aid in making important strategic decisions, such as targeting under-served areas or launching new stores.

4. Real Estate Investment and Development: Real estate investors and developers can use the dataset to find promising areas for commercial development, identify potential retail spaces, and make informed investment decisions based on the store distribution in neighborhoods.

5. Augmented Reality Applications: Developers of AR applications can use the dataset to create AR experiences that provide information about nearby stores, such as store ratings, opening hours, and special offers, to users in real time as they navigate through the streets using their devices.

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.

Collection of static general Bloomington and city street maps of various sizes.

This web map references the live tiled map service from the OpenStreetMap (OSM) 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 and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: https://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. 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. Tip: Here are some well known locations as they appear in this web map, accessed by launching the web map with a URL that contains location parameters: Athens, Cairo, Jakarta, Moscow, Mumbai, Nairobi, Paris, Rio De Janeiro, Shanghai

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources. We've made available a number of tables (explained in detail below): history_* tables: full history of OSM objects planet_* tables: snapshot of current OSM objects as of Nov 2019 The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing. Example analyses are given below. This dataset is part of a larger effort to make data available in BigQuery through the Google Cloud Public Datasets program . OSM itself is produced as a public good by volunteers, and there are no guarantees about data quality. Interested in learning more about how these data were brought into BigQuery and how you can use them? Check out the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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