IoT-Integrated AI System for Dump Site Detection via Aerial Imagery & Gesture-Based Interaction

Detect dump sites from drone or satellite imagery with high accuracy.

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Seamless integration with IoT-enabled gesture devices for field operability.

Converts GeoJSON coordinates into image pixels for precise detection zones.

YOLO model optimized for identifying waste zones in complex environments.

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Manual waste site identification from aerial images is time-consuming and inconsistent. Moreover, in-field operators often lack user-friendly tools to interact with data on the go. The client needed an AI system capable of accurate dump detection combined with gesture-enabled functionality for on-site navigation and input—without relying on touch interfaces.

NeuraMonks delivered a dual-layered solution:

AI Layer:

A deep learning model (YOLO) trained on preprocessed .tif images using real-world GeoJSON coordinates.

IoT Layer:

A gesture-based input system allowing field personnel to interact with detection results and map overlays without manual device use.

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This enabled seamless human-AI collaboration, speeding up decision-making in environmental inspections.

Ensuring accurate transformation of geographic coordinates into pixel-perfect regions.

Maintaining detection precision across varied terrains and imagery scales.

Integrating real-time IoT gesture signals with the visual interface without lag.

Handling noise and false triggers in gesture inputs during field deployment.