The current landscape of modern field robotics confronts a notable expansionchallenge, primarily stemming from the proliferation of diverse platforms, vast data vol- umes, sophisticated algorithms, and powerful computational resources. However, the progress of robotics beyond its conventional role as a mere observational tool is im- peded by limited autonomy and undeveloped data inference capabilities. The scarcity of labeled data poses a significant hurdle in the application of machine learning to novel tasks in the space of image comprehension, which incur exorbitant costs for manual labeling. The traditional approach of relying on human annotators at workstations for prolonged periods of time exhibits several limitations, including subjective consis- tency and human factors concerns. Conversely, the adoption of human-centric labeling tools presents a viable solution to address these challenges by deploying human experts in the field to perform data labeling, thereby enabling swift inspection of the sensed environment while eliminating data representation as a bottleneck. This approach fa- cilitates the rapid annotation of data in a sensor-agnostic manner, culminating in the generation of large-scale, high-quality datasets within feasible timeframes by leveraging comparatively small amounts of expert-derived knowledge to improve data labels over all.