Google Maps Kenya Street View
Fajar Hawkins
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We currently have 3D maps available for routes and although this doesn't match the level of detail that street view does on Google Maps, it could offer a general sense of what the streets are like when paired with the Satellite map overlay.
+1
Was going to suggest this and saw that the idea was already submitted.
I want to explore a new trail tomorrow morning and it would be great if Street View extended to mountains and trails. The community can submit photos and videos of the different routes.
Other routing apps have street view integrated and I use it often to make sure the routes I create on Strava is good..when i don't know the area so that i dont find any surprises when I am riding. It would be great to see this feature on strava sometime.
Hello good people, I have captured an Isometric street view using the street view functionality in Google earth and in my model in Infraworks, I would like to import such an Iso view to make my model realistic since I dont have any pont data/drone images to import, is it really possible to import and view such ISO street views from google earth into Infraworks, I will truly appreciate your responses, thanks in advance, I have attached such an image
If you had point data available for latitudes and longitudes (from a shape file with point data of the points of interest), you could run a script to bring up Google Maps with a satellite display from which a street view is easily obtained by "zooming in". Let me know if you have such a point file available and I can share notes on the type of script you need.
I really appreciate your response, I lack a point data/shape file for the town which I need called Migori, a town in Kenya in the East African region, I have tried looking over the internet and I just dont know where I can find some point data/shape file to upload into my Infraworks model to make it have a realistic outlook of the buildings and the streets and all that, so does this mean that even street view from google maps is not going to help me at all while inside Infraworks?, or could you be knowing how or where I can get some point data/shape files for use in Infraworks?, once again I really appreciate you taking your time to help me out
The Mathare Valley, shown here in an aerial map, is one of the largest and oldest slums in Nairobi, Kenya. Residents are using hand-held GPS devices to map the area, which comprises 13 villages and is home to nearly 200,000 people. Courtesy of Muungano Support Trust and Jason Corburn, UC Berkeley hide caption
Slums by nature are unplanned, primordial cities, the opposite of well-ordered city grids. Squatters rights rule, and woe to the visitor who ventures in without permission. But last year, a group of activist cartographers called the Spatial Collective started walking around Mathare typing landmarks into hand-held GPS devices.
Isaac Mutisya, whom everyone knows as Kaka, points out the spot in Mathare where he was born. The more he maps his slum through the lens of his GPS, the more he feels the outside world is finally looking back. Gregory Warner/NPR hide caption
Their map includes things like informal schools, storefront churches and day care centers, but also dark corners with no streetlights, illegal dumping grounds and broken manholes. They bring the most urgent problems to the attention of the authorities.
We think of GPS maps as guides. They are the sometimes annoying, always calm, recorded voice in our car that steers us through unfamiliar places. But maps are also public records that can help slum dwellers negotiate with city authorities.
The residents of Nairobi's informal settlements live in unsafe, overcrowded and often unsanitary housing and lack access to basic services such as sanitation, water and electricity. Courtesy of Muungano Support Trust and Jason Corburn, UC Berkeley hide caption
But Wangari's map looks more like a mad game of Tetris. Blocks of every shape are jammed in together with no space between, except narrow pathways following the trails of open sewers. And every year these narrow streets get narrower still, as people expand their houses farther into the walkway.
If Kaka's map, the Spatial Collective map, is a map of city neglect, Wangari's map describes life in a slum where the idea of public space has no enforceable authority. You'll find no parks, no playgrounds, no breathing room.
This year Wangari did use her map to briefly claim some communal space. The story is this: After years of grass-roots activism, the city of Nairobi finally agreed to pipe in municipal water and sell it at public collection points for a half a penny on the gallon. But when the city workers went to lay the pipes, the place was so crowded they couldn't actually find enough space for their shovels.
Slum mapper Emily Wangari stands outside a communal toilet in the Kiamutisya settlement of Mathare. This settlement has only four toilets for 4,000 residents. By mapping the problems, she hopes to pressure authorities to bring in more necessary services. Gregory Warner/NPR hide caption
In the storage room of an Internet cafe that the Spatial Collective uses for its office, I watch Kaka and the other slum mappers play idly with their GPS devices. In nine clicks, they zoom out the view broader and broader to encompass Nairobi city, then Kenya, then Africa, then the globe. Kaka laughs when I point out his habit.
While basic inadequacies and deep uncertainty still define the life here, he says, the days when some unscrupulous developer could send arsonists in at night and erase all traces of a community seem to be fading into the past. Among residents, there's a growing sense that in seeing their slum from the satellite level, from 10,000 miles up, they are starting to take their city out of the shadows.
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Identifying road surface types (paved or unpaved) can ensure road vehicle safety, reduce energy consumption, and promote economic development. Existing studies identified road surface types by using sensors mounted on mobile devices and high-resolution satellite images that are not openly accessible, which makes it difficult to apply them to large-scale (e.g., national or regional) study areas. Addressing this issue, this study developed a dataset of road surface types (paved and unpaved) for the national road network of Kenya, containing 1,267,818 road segments classified as paved or unpaved. To accomplish this, this study proposes a method that integrates crowdsourced geographic data (OpenStreetMap) and Google satellite imagery to identify road surface types. The accuracy, recall, and F1 score of the method were all above 0.94, validating the effectiveness of the method. The data sources of the method are freely available, and the method may be applied to other countries and regions. The dataset developed based on the method can provide data support and decision support for local governments to improve road infrastructure.
Currently, there are many studies on identifying road surface types. For example, Abbondati et al. developed a system that uses a GPS receiver and a triaxial accelerometer mounted on a mobile device to detect road surface types11. De Blasiis et al. proposed an algorithm to determine road surface roughness and road defect types, namely potholes and swells/shoves, based on 3D point cloud data collected by a vehicle-mounted radar system12. Shon et al. developed a self-monitoring road management system to detect road surface types (paved and unpaved) on the Korea-Japan Highway13. Staniek developed a system to collect data from smartphone users and used the city of Tychy in Poland as an example to assess road defects, namely unpaved roads categorized into gravel and earth roads, by analyzing the dynamics of vehicle motion14. These studies are all based on sensors mounted on mobile devices to monitor road surface types.
However, there are still several shortcomings in the existing methods. First, the method of integrating sensors into mobile devices to monitor road surface type requires field data collection. However, this process is not only time-consuming but also difficult to implement in a large-scale area (such as national scale)19. Although satellite remote sensing imagery are widely regarded as a powerful tool for conducting large-scale studies, identifying road surface type can be challenging with medium and low-resolution remote sensing imagery. Thus, most of existing studies used high-resolution remote sensing data (e.g., below 1 meter), but these data are not open to the public, it is therefore hard to utilize the data into a different study area. Furthermore, object extraction and classification from remote sensing images often demands a substantial number of training samples. Notably, training samples derived from a singular region or dataset may not be universally applicable to other regions. Thus, the ongoing research challenge is to develop a method for automatically and adaptively identifying road surface type at a large scale.
In recent years, geographic data, e.g. OpenStreetMap (OSM), edited and updated by global volunteers has been considered as an important source of obtaining global geographic information. With the advantages of free access, global coverage, and rich geographic features, OSM data provides the possibility of obtaining training samples for road surface type at the national and even global scale20,21. On the other hand, high-resolution Google satellite imagery not only provides rich information on the earth but also has the feature of global open access.
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