Bikeable is our neural network that is able to retrieve a safety score (0-10) from an input image. This AI model was trained leveraging the perception of safety of thousands of cyclists globally. Using Google Street View images, we create safety maps for cities within a few seconds. Below, you can find multiple points identified by their geographical coordinates and with a safety score associated (as low, as more unsafe).
| # | Latitude | Longitude | Safety Score | City |
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Documentation for API Call:
Each point in the table was manually inserted by a user in our route planner. Alongside their geographical location, there is a required reason to be classified as dangerous. Users can up or down vote existent points, avoiding the unnecessary insertion of more in the same location.
| # | Latitude | Longitude | Votes number | City | Address | Solved | Up votes | Down votes | Description |
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Documentation for API Call:
Believing in the multimodality as the future of mobility, Lisbon users can have their most efficient cycling routes calculated in Lisbon according to their relative location to the closest Gira stations. This optimizes walking and cycling distances from current location until destination point, while offering an hybrid route.
| # | Latitude | Longitude | Location | No. Bicycles | No. Docs | Date | State | Service Type | Ratio |
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Documentation for API Call:
This table contains the safety data aggregated per neghborhoods.
| # | Name | Safety Score | Standard deviation | No. Points | Country | City |
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Documentation for API Call: