Jakarta, INTI - BRIN and BNN are developing an illegal cannabis field detection and mapping system powered by Artificial Intelligence, satellite imagery, and Drone Industry. The cross-disciplinary collaboration aims to improve the effectiveness of identifying cannabis plantations in remote areas across Indonesia.
According to the Indonesia Drug Report 2025 published by BNN’s Research, Data, and Information Center, Indonesia recorded 46,748 narcotics-related criminal cases, with cannabis ranking as the second most common drug-related case after methamphetamine, totaling 3,814 cases.
Associate Engineer at BRIN’s Research Center for Aviation Technology, Yomi Guno, explained that Indonesia’s vast and difficult geographical terrain presents major challenges for accurately mapping cannabis cultivation areas through conventional ground operations alone.
Yomi stated that the collaboration is expected to integrate remote sensing technology, geographic information systems, UAV technology, and AI to support the mapping and identification of cannabis cultivation areas as part of efforts to modernize Indonesia’s illegal plantation detection and monitoring systems.
The initiative involves several BRIN research centers, including the Research Center for Geoinformatics, the Research Center for Data Science and Information, the Research Center for Artificial Intelligence and Cyber Security, and the Research Center for Aviation Technology.
BRIN and BNN Build Integrated Geospatial Monitoring System
In the initial phase, BNN identifies research needs based on intelligence reports and field data related to suspected illegal cannabis plantations. BRIN’s Geoinformatics Research Center then conducts preliminary analysis using high-resolution satellite imagery to identify areas with characteristics suitable for cannabis cultivation.
The next stage is carried out by BRIN’s Aviation Technology Research Center through UAV field operations. This process includes validating areas identified through satellite imagery while simultaneously collecting high-resolution aerial photogrammetry data.
According to Yomi, the field data is further processed by BRIN’s Data Science and AI research teams using specialized photogrammetry software to produce orthomosaic stitching, a geometrically and geographically corrected high-resolution orthophoto map composite.
He explained that the final research output can be directly utilized by BNN to support law enforcement activities. BRIN provides high-precision geospatial documents, including orthomosaic datasets, spatial data on cannabis cultivation areas, and priority area recommendations for eradication operations in accordance with applicable laws.
Yomi emphasized that the cross-disciplinary and cross-institutional collaboration reflects BRIN’s commitment to supporting efforts to combat illegal cannabis plantations in Indonesia.
Conclusion
The collaboration between BRIN and BNN highlights Indonesia’s growing use of AI, satellite technology, and UAV systems to modernize illegal cannabis detection and monitoring. Through integrated geospatial analysis and aerial surveillance, the initiative is expected to strengthen law enforcement effectiveness and support national anti-drug efforts.
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