The grade or slope of a road plays an important role in shaping walking and active mobility behaviors, such as cycling and scootering, by influencing route choice and safety. For individuals with mobility challenges, road slope significantly impacts their ability to navigate streets and sidewalks safely and efficiently. However, the absence of precise road grade data creates barriers to providing accurate travel information and conducting comprehensive analyses. Crowdsensing offers a cost-effective solution for gathering sidewalk elevation data, but validating and fine-tuning this crowd-sensed data remains a challenge. This paper presents a solution to address accessibility for all users, promoting the development of inclusive urban infrastructure and equitable smart cities by introducing a real-time, budget-friendly method for algorithm tuning. Using the motion sensors method, we collect ground truth data to serve as a benchmark for fine-tuning discrepancies in crowd-sensed data. By integrating large volumes of ground truth data alongside lower-quality crowd-sensed data from smartphones, the algorithm could be continuously refined, enabling more accurate predictions of road grades and elevation. This feasibility study provides a roadmap to a scalable and cost-efficient validation method that enhances the reliability of crowdsourced elevation data and contributes to the creation of high-resolution, real-time ground truth data for urban accessibility studies.