Bee Edge AI

#Bee Edge AI

Program the Bee camera to collect data from the physical world you want

Build and deploy custom AI workloads on the Bee's edge computing platform. Write Python modules, push them OTA to Bee devices worldwide or your own devices, and stream results to your cloud.

#Key Differentiation

• Edge compute, not cloud latency -- Run object detection and ML inference directly on the 5.1 TOPS NPU. Results in milliseconds, not round-trips.
• Zero hardware and telecom ops -- Deploy to a global fleet instantly. No procurement, no installation, no firmware headaches.
• Programmable data collection -- Change what you capture with a code push. New use case? New model? Deploy OTA in hours, not hardware cycles.

#Pricing

• See pricing here

#Overview

Edge Modules are Python programs that run on the Bee camera to collect the data you need from the physical world. They have access to all onboard sensors and can run custom ML models alongside the native Map AI stack.

#Capabilities

#Sensor Access

Edge Modules can access all onboard sensors:

#Running Custom Models

Run object detection and classification directly on the edge. The 5.1 TOPS NPU handles inference offline -- no cloud dependency.

#Geographic Targeting

Deploy modules to specific regions. Devices receive your module only when operating within targeted areas.

#Data Offload

Edge Module output streams to your cloud via Bee Connectivity Services, or upload to Bee Maps and consume via API.

#Connectivity Channels

#Output Configuration

#Deployment Workflow

1. Create Module -- Define configuration and upload your model via Bee Maps console
2. Configure Output -- Set your endpoint and select data types
3. Set Targeting -- Define geographic regions
4. Staging Deploy -- Push to a small device subset to validate accuracy
5. Production Deploy -- Roll out to full target region via OTA

#Example Use Cases

#Retail & Places Churn

Monitor storefronts to detect business changes -- new openings, closures, rebrands.

What you detect: "For lease" signs, changed storefront signage, boarded windows, new business openings

Output: Structured change events with imagery, fed into places databases to keep POI data fresh without manual surveys.

#Complex Intersection Video

Capture video clips at specific intersections for traffic analysis, urban planning, or safety studies.

How it works: Define target intersections via GeoJSON or auto-detect based on traffic light count. Trigger recording when devices enter the zone. Collect multi-angle footage as different vehicles traverse the same intersection over time.

Output: Geotagged video clips from multiple perspectives, timestamped for temporal analysis.

#Long-Tail Event Capture

Detect rare but critical events for autonomous vehicle training and safety validation.

Example events: Pedestrians with strollers, wheelchair users crossing, animals in road, unusual vehicle types, construction zone edge cases, adverse weather conditions

How it works: Run lightweight classifiers on-device. Upload only when target events are detected. Build datasets of real-world edge cases at global scale.

Output: Annotated imagery and video of rare events, with full sensor context.

#World Model Training Data

Collect synchronized video + depth + IMU data for training world models and vision foundation models.

Data captured: High-resolution video (12.3MP @ 30fps), stereo depth imagery, full IMU telemetry, precise GPS positioning

Targeting options: Road types (highway, urban, rural), weather conditions, geographic regions, time of day

Output: Synchronized multimodal sensor data at scale -- the raw ingredients for physical world simulations.

#Developer SDK

Build and test Edge Modules locally before deployment.

Repository: github.com/Hivemapper/bee-plugins

#Quick Start

# Install dependencies
python3 -m pip install -r requirements.txt

# Build your plugin
bash build.sh [output_name] [entrypoint]

#Local Development

Connect to the Bee over WiFi (password: hivemapper) and iterate locally with hot-reload.

#Fixture Data

Test with pre-built datasets before deploying to real devices:

#Device Utilities

# Get calibration data for accurate positioning
python3 device.py -C > calibration.json

# Network management
python3 device.py -Wi <ssid> -P <password>  # Switch to WiFi
python3 device.py -L                         # Switch to LTE
python3 device.py -Ws                        # Scan WiFi networks

#Getting Started

1. Clone the SDK: github.com/Hivemapper/bee-plugins
2. Build and test locally with fixture data
3. Email us to get API credentials and deploy