Expected outcomes of this project include:
A GPT-4-powered drone assistant that translates mission briefs and real-time patrol data into actionable compliance assessments.
An automated system that interprets environmental incident descriptions and generates recommended UAV flight protocols and image targeting guidelines.
Multilingual templates for regulatory notices, patrol summaries, and interdepartmental correspondence.
A broader demonstration of how LLMs can integrate with computer vision, GIS, and remote sensing systems to support AI-assisted environmental protection and law enforcement.
This project offers a scalable model for the application of GPT-based AI in smart, field-deployable environmental monitoring systems.
Fine-tuning GPT-4 is essential because drone-based environmental patrol involves domain-specific technical language, location-sensitive regulations, and mission-specific visual-linguistic correlations. GPT-3.5 lacks exposure to UAV flight manuals, geospatial pollution descriptors, and regulatory enforcement texts. Fine-tuning GPT-4 will allow the model to:
Match unstructured descriptions of pollution events to aerial imagery and sensor data;
Interpret local flight restrictions and convert patrol rules into dynamic mission plans;
Generate legally sound inspection reports and enforcement communications across regions and agencies.
Without fine-tuning, GPT-4 would lack the spatial reasoning, regulatory depth, and multimodal contextual grounding needed
