3D scanning technology has evolved into a new era. In addition to stationary body scanners that typically use infrared-based measuring mechanisms, remote 3D body scanning apps utilizing advanced computational technologies such as computer vision, machine learning, and 3D modeling have gained popularity. This surge in use and research interest spans applications in health, fitness, virtual apparel fitting, and more. Remote scanning apps also present new opportunities for designing and developing personal protective equipment and clothing (PPE and PPC), where measuring widely dispersed wearers at scale has traditionally been challenging, limiting improvements in fit. The population of U.S. female firefighters exemplifies this challenge. This study aims to assess the accuracy of full-body measurements obtained via a remote app-based body scanner to support the development of a U.S. female firefighter database for improving PPE and PPC construction, sizing, and fit. Thirty-six active-duty U.S. female firefighters were scanned using both a remote, mobile app scanner and a stationary body scanner. Accuracy was assessed through 48 corresponding body measurements covering key circumferential and length dimensions relevant to apparel. Procrustes analysis, regression analysis, and Multivariate Analysis of Variance (MANOVA) were applied to examine measurement relationships between the two scanning methods. Results showed that, for most body measurements, the app scanner produced highly comparable results to the stationary scanner, regardless of measurement extraction method. However, specific measurements—such as shoulder, arm, waist, abdomen, and ankle—require additional manual landmark verification. These findings ensure high-quality data for the U.S. female firefighter anthropometric database and future remote scanning applications.