Download Free Google Maps Street View Images

Making a strong impression on our list of funny Google street views is this penguin that was caught in Western Australia, being pulled by a man on a penny-farthing bike. Click here to see it on Google street view.

We are getting a kick out of some of these funny Google street views! How about this one that features a bear getting ready to have his dinner in Kurile Lake, in Russia. Click here to see it on Google street view.

Download Google Maps Street View Images

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I am using the Google Street View Image API to display an image of an address. Although most of the images are amazingly accurate, I've noticed a few that are from the wrong angle like a house on the corner where the image is from the side street, not from the front street. When I check Google Maps the image that shows on the top of the left panel is correct.

Recent additions to Google Maps' Street View imagery have been "off the map", and only accessible via the panorama id (the panoid URL parameter), such as these hot-tub monkeys or this donkey. If you remove the panoid from the URL then the monkeys don't load. For comparison, this picture of a tree doesn't need the panoid to load the streetview panorama.

I can understand why having two dates could be confusing, but if the view date follows the URL, as in ( : viewed 8 September 2015), wouldn't that clarify things? Especially since Google images seem to change frequently.

Google encourages the inclusion of "active" maps and street-views (i.e. ones that can be interacted with) using an HTML iframe element. I won't go into any technical detail here but an article on making use of this feature in Blogger can be found at -viewpoint.blogspot.com/2014/01/using-google-maps-with-blogger.html.

Short answer:

a street sign is not copyrighted; a photo is copyrightable; a street sign inside a photo is not copyrightable.

Long answer:

Aerial imagery and streetView are not comparable.

In first case, images are rectified using complex algorithms (compensate distorsions with Digital Elevation Model, positions and angles from the camera, noise and colors filtering, etc). Tracing on them is not allowed without permission because you don't copy facts only, you also copy those georeferencing information.

In second case, when you read a street sign on a StreetView picture, you use the content of the picture, not the picture itself. This is 'fact', the street sign is not copyrighted by Google, neither Google made any creation on that content. It's true that to find the picture and display it on your browser, the StreetView interface had to use the image georeferences. But this is a 'mean' to find the picture, not the content of the picture, this is not what you are copying for OSM (which is again just the street name, a 'fact').

If you need some evidence, it is always possible to detect copies from aerial imagery since each provider has small artefacts which can be detected in the copy. When you copy a street sign, it is simply impossible to detect if it's coming from SV or a normal survey. People saying that it's not explicitely allowed and then forbiden without any evidence about creation or added value violation are just spreading FUD.

A google arial photograh as its possible to see by using the street maps, is just a good comparison to refresh your memory and yes sometimes 'old'.I even have the impression that the streets in appearing in Google have the same errors in the basic layer for OSM. Its like looking at your neighbour in the classroom and coppying the wrong answers and getting the question afterwards 'were you able to see it' and both end up with a low value.

Maintaining an up-to-date record of the number, type, location, and condition of high-quantity low-cost roadway assets such as traffic signs is critical to transportation inventory management systems. While, databases such as Google Street View contain street-level images of all traffic signs and are updated regularly, their potential for creating an inventory databases has not been fully explored. The key benefit of such databases is that once traffic signs are detected, their geographic coordinates can also be derived and visualized within the same platform.

Today, several online services collect street-level panoramic images on a truly massive scale. Examples include Google Street View, Microsoft street side, Mapjack, Everyescape, and Cyclomedia Globspotter. The availability of these databases offers the possibility to perform automated surveying of traffic signs (Balali et al. 2015; I. Creusen and Hazelhoff 2012) and address the current problems. In particular, using Google Street View images can reduce the number of redundant enterprise information systems that collect and manage traffic inventories. Applying computer vision methods to these large collections of images has potential to create the necessary inventories more efficiently. One has to keep in mind that beyond changes in illumination, clutter/occlusions, varying positions and orientations, the intra-class variability can challenge the task of automated traffic sign detection and classification.

In recent years, several vision-based driver assistance systems capable of sign detection and classification (on a limited basis) have become commercially available. Nevertheless these systems do not benefit from Google Street View images for traffic sign detection and classification. This is because these systems need to perform in real-time and thus leverage high frame rate methods such as optical flow or edge detection methods are not applicable to the relatively coarse temporal resolutions available in Google Street View images (Salmen et al. 2012). Several recent studies have shown that Histograms of Oriented Gradients (HOG) and Haar wavelets could be more accurate alternatives for characterization of traffic signs in street level images (Hoferlin and Zimmermann 2009; Ruta et al. 2007; Wu and Tsai 2006). For example (Z. Hu and Tsai 2011; Prisacariu et al. 2010) characterize signs by combining edge and Haar-like features, and (Houben et al. 2013; Mathias et al. 2013; Overett et al. 2014) leverages HOG features. More recent studies such as (Balali and Golparvar-Fard 2015a; I. M. Creusen et al. 2010) augment HOG features with color histograms to leverage both texture/pattern and color information for sign characterizations. The selection of a machine learning method for sign classification is constrained to the choice of features. Cascaded classifiers are traditionally used with Haar-like features (Balali and Golparvar-Fard 2014; Prisacariu et al. 2010). Support Vector Machines (SVM) (I. M. Creusen et al. 2010; Jahangiri and Rakha 2014; Xie et al. 2009), neural networks, and cascaded classifiers trained with some type of boosting (Balali and Golparvar-Fard 2015a; Overett et al. 2014; Pettersson et al. 2008) are used for classification of traffic signs.

This paper presents a new system for creating and mapping inventories of traffic signs using Google Street View images. As shown in Fig. 1, the system does not require additional field data collection beyond the availability of Google Street View images. Rather by processing images extracted from Google Street View API using a computer vision method, traffic signs are detected and categorized into four categories of regulatory, warning, stop, and yield signs. The most probable location of each detected traffic sign is also visualized using heat maps on Google Earth. Several data mining interfaces are also provided that allow for better management of the traffic sign inventories. The key components of the system are presented in the following:

In the developed web-based platform, Google map interface is used to visualize the spatial data and the relationships between different signs and their characteristics. More specifically, a dynamic ASP .NET webpage is developed based on the fusion table and a clustering package that visualizes the result of detected signs on Google Map, Street View, and Earth, by calling needed data using queries from the SQL database and the JSON files. A javascript is developed to sync a Google map interface with three other views of Google Map, Street View, and Earth (See Fig. 8). Markers are added for the derived location of each detected sign in this Google Map interface. A user can click on these markers to query the top view (Goole map view), bird-eye view (Google Earth view), and street-level view of the detected sign in the other three frames. In the developed interface, two scenarios can happen based on the size of traffic signs and the distance between each two consecutive images taken in the road:

Figure 10 shows an example of the dynamic heat maps which visualizes the most probable 3D locations for the detected traffic signs. As one gets close to a sign, the most probable location is visualized using a line perpendicular to the road axis. This is because the GPS data cannot differentiate whether a detected sign is on the right side of the road, is over mounted in the middle of the view or is on far left. Figure 11 presents the Pseudo code for mining and representing traffic signs information.

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale. e24fc04721