Skip Scooters provides reliable last-mile transportation by offering shared electric scooters. The company works with cities to ensure fleets are operated with the support of the surrounding communities.

Skip believes in working together with transportation departments to ensure their fleets and riders are complying with local regulations.

Each city provides a unique set of regulations they expect Skip’s riders to comply with. The business-critical problem Skip focused on solving is ensuring local parking regulations are met by the riders. They prompt the riders to upload a picture of the parked scooter at the end of every ride. The challenge is analyzing these images in real-time against local parking regulations to determine compliance. Manually analyzing these images is time-consuming and cost-prohibitive in the long run.

Skip decided to automate the enforcement of local parking regulations by creating a machine learning model that can classify images. To train the model, they needed training and validation data annotated according to specific instruction sets. The annotation task would entail classifying whether an image of a parked scooter is in compliance with the local regulations. The instructions vary based on the city, resulting in a complex data annotation project.

The image classification solution helps Skip to determine parking compliance within seconds, providing riders immediate feedback, resulting in better rider experience. It also significantly reduces the cost of enforcing compliance in the long run.