Enviro News Asia, Yogyakarta – The Faculty of Geography at Gadjah Mada University (UGM) Yogyakarta held a Geo-AI System Training for Carbon Storage Assessment from June 23–26, 2025, in Yogyakarta.
Attended by 60 participants from various institutions, the training was organized to support the FOLU Net Sink 2030 program.
The training was hosted by the Faculty of Geography at UGM with full support from the Ministry of Forestry, the Ministry of Environment and Climate of Norway, and the Environmental Fund Management Agency (BPDLH).
Dean of the Faculty of Geography UGM, Prof. Muhammad Kamal, who officially opened the event, stated that the four-day training program was equivalent to 6 academic credits.
The training aimed to enhance participants’ understanding and skills in applying Geo-AI-based geospatial technologies, particularly for carbon valuation and environmental management.
Topics covered included Python programming for geospatial data, deep learning with U-Net algorithms, as well as hands-on practice in model development and AI-based data processing.
In addition to theoretical sessions, participants also engaged in fieldwork at the Wanagama Educational Forest for carbon measurement and conducted LiDAR data collection using UAVs in Bunder Grand Forest Park (Tahura Bunder).
The collected data was then processed and analyzed as a simulation of Geo-AI application for climate change mitigation.
Syafiq M. Ridha, one of the participants from the Environmental Control Center (Pusdal LH) of Central Java, said, “This training provided new insights into using Geo-AI for carbon stock analysis.
The intensive materials offered a comprehensive understanding of AI, machine learning, remote sensing, carbon stock assessment, and UAV technology.” He expressed hope that the knowledge gained could be applied in environmental management activities.
At the end of the training, M. Mangku Parasdyo from Pusdal LH Central Java received an award as the best participant.
It is hoped that this training will help participants optimize the use of Geo-AI technologies to support carbon emission reductions and effectively achieve the NDC and FOLU Net Sink 2030 targets. (*).
