For our cleaning use case demo (Supplementary Materials), we considered a meeting room as an IndoorGML node (Room 326) with a four-person capacity. For this demo, the OGC indoorGML was used as it offered the following advantages: IndoorGML cells were defined as the geofence; the geometry and area of each cell (geofence) were calculated and the location of each indoorGML cell (the centroid of the geofence) was used for the enrichment of the camera data. The number of people entering or exiting each cell was monitored. People in each frame were detected in real-time using a pre-trained You Only Look Once (YOLO) model [62] and the results were then published as an MQTT message to the AWS IoT Core. On the backend, the maximum allowed people in a cell, or cell capacity, was either assigned by the building management, or calculated by dividing the cell area into squares of six feet two inches. The “Gathering Restriction”—the number of people over each IndoorGML node—was then calculated. This value changes over a range of 0–1 based on the number of people divided by the capacity of the room. Should the number of people exceed the cell capacity, a Gathering Restriction alarm would be generated for the cell. The following figure (Figure 10) shows a frame of the meeting room, detected people, and Gathering Restriction alarm. The video demo of this scene is attached in Supplementary Materials which shows the people count online when they enter or exit the room.