Design

a modular DSS architecture integrating UAV data, analytics, and spatial visualisation​.

Implement

preliminary AI-based models for exploratory analysis of forest cover variations

Support

qualitative prioritisation of areas potentially requiring further inspection

FAMMS

FAMMS aims to design and implement a functional prototype that supports exploratory monitoring of illegal logging risks by integrating UAV imagery, preliminary AI-based analytics, and GIS-enabled decision-support tools. 

waves

DJI Phantom 4 Pro V2.0

1-inch CMOS Sensor

Model 1: Change Detection

Model 2:Data Acquisition Campaigns, AI Analytics Models 

Outcomes

Functional DSS prototype integrating UAV data, analytics, and GIS visualisation​.

End-to-end workflow implementation from data acquisition to decision-support outputs​

Preliminary analytical components integrated and tested qualitatively​

Clear identification of validation gaps and technical limitations​

GO2Market strategy 

Challenges Faced

Cadastral Data Limitations​ - Impossibility to use cadastral data effectively due to access restrictions or formatting inconsistencies, hindering precise property boundary delineation.

Environmental Variability​ - Significant environmental variability affecting data consistency across acquisition campaigns, requiring robust normalization techniques for reliable analysis.

Impact (Socio-economic & Environmental)

Environmental Impact

Significantly improved situational awareness for illegal logging monitoring operations. By integrating UAV data with AI analytics, the system allows for faster detection of canopy variations and potential unauthorized harvesting activities, contributing to forest preservation efforts.

Socio-economic

Strategic support for more informed planning of inspection activities and resource allocation. Optimizing patrol routes and focusing human resources on high-risk areas reduces operational costs and increases the effectiveness of law enforcement and forest management bodies.