Nimble Maps
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A project that aims to solve three major problems of India i.e. Potholes, Illumination (Women Safety at Night) and Noise graph. We plotted each and every parameter in a single map. Crowdsourcing is the best way to collect data so to implement that user input is also taken to improve our dataset thro ...learn more
Project status: Under Development
Overview / Usage
Tracking of potholes and measurement of Noise level (dB) & illumination level (Lux) in cities and mapping these on Google maps – Smart City enabler ‘ - Develop a system to measure and map noise and illumination levels in a city along with the tracking of potholes for the comfort and safety of commuters as well as improvement of urban planning.
Methodology / Approach
Noise data from all over the city will be mapped and color-coded. Then classification will be done based categories with the use of Machine Learning algorithms which will produce a color shade over the map (each shade pertains to a noise level). This will then be interfaced with any popular GPS map application to be used as a feature at users’ requirements.
An API will be responsible for collecting Potholes and illumination data which will be taken by all the users with coordinate information(extracted by GPS). The validation of the data will be done and then it will be categorized based on illumination level/pothole size. Each of which will be placed in the same GPS map application. For illumination street lights and building, lights will be used and the pothole's information will be taken as a shred of photo evidence with coordinate (latitude & longitude) information both of which will then be plotted on the map.
Technologies Used
● Apps that can facilitate direct information for the dataset which will be used after validation by crowd validation and categorized by Machine Learning Techniques
● Initial data for the system will be taken for a 24 Hr timeline so as to present the factors based on the time the day (illumination and noise level are subject to change at day and night)
● User can view the changes of noise and illumination level based on day and night time making the system much more accurate and safe from outliner data
● The dataset is easy to update making the system predictions highly accurate with the real world. Dynamic updates on the map with respect to time
● Blockchain network will be used in the improvement phase to validate the data provided by the user and removal of duplicate data provided by different users