Jakarta, August 2026 — The Office of Chief Economist (OCE) Group of Bank Mandiri has completed an intensive eight-week Geographic Information System (GIS) training program designed to strengthen the team’s capacity in spatial data analysis and support more effective, sustainable risk assessment in the palm oil sector. Held every Friday afternoon from June 26 to August 7, 2026, a team delivered the training program from the School of Architecture, Planning and Policy Development (SAPPK), Institut Teknologi Bandung (ITB). The team was led by Nurrohman Wijaya, Ph.D., with Diaz Ekaputra, S.T. and Deni Marsel Susanto, S.T., serving as assistant trainers. The training combined fundamental GIS and remote sensing concepts with practical applications relevant to banking, particularly spatial monitoring, environmental compliance, and credit-risk assessment in the palm oil sector.
Strengthening Geospatial Capacity for Sustainable Finance
Indonesia’s palm oil industry plays an important role in the national economy while facing increasing requirements related to environmental protection, forest management, and Environmental, Social, and Governance (ESG) standards. For financial institutions, the ability to independently assess plantation locations and identify potential overlaps with protected or restricted areas is increasingly important. Through the training, the OCE Group developed greater capacity to conduct geospatial assessments independently, reducing reliance on external spatial analysis and strengthening the institution’s ability to monitor environmental and deforestation-related risks within its portfolio.
Eight Weeks of Applied GIS Training
The 32-hour program was conducted through offline sessions at Wisma Danantara Indonesia, Jakarta, combining classroom instruction with practical exercises. Participants worked with QGIS, Google Earth Engine (GEE), and Google Colab to develop an end-to-end spatial analysis workflow. The training was organized into three main phases:
Phase 1: GIS Fundamentals and Policy Integration
Participants learned fundamental concepts including map interpretation, coordinate reference systems, UTM, and Indonesia’s One Map Policy. Practical sessions covered QGIS, vector and raster data management, and access to official administrative spatial data from the Geospatial Information Agency (BIG).
Phase 2: Geoprocessing and Spatial Analysis
The second phase introduced raster analysis, digital elevation models (DEM), and multi-criteria evaluation (MCE). Participants also practiced buffer analysis for river protection areas and spatial overlay analysis to identify potential overlaps between plantation concessions and relevant government datasets.
Phase 3: Remote Sensing, Cloud Computing, and AI
The final phase focused on satellite remote sensing, vegetation indices, time-series analysis, and land-cover classification. Participants used Google Earth Engine to process Sentinel-2 imagery and explored a pre-trained DeepLabV3+ deep learning model to identify palm oil plantation areas in Sumatra and Kalimantan.
In the final stage, participants integrated their geospatial outputs with Google Colab to calculate area statistics, generate structured datasets, and examine land-cover changes over multiple years.
From Spatial Analysis to Executive Decision-Making
The program concluded with final project presentations in which participants demonstrated their ability to independently conduct geospatial analysis and translate spatial data into decision-support information. The resulting outputs included multi-temporal plantation maps, statistical summaries of land distribution by province and district, and spatial analyses identifying potential overlaps and environmental risks. These outputs can provide valuable evidence for portfolio monitoring and support more informed decision-making by credit and risk-management teams. The completion of the training represents an important step in strengthening the OCE Group’s internal geospatial capabilities. By combining GIS, remote sensing, cloud computing, and artificial intelligence, the program supports the development of more data-driven, transparent, and environmentally informed approaches to sustainable financing and risk management in the agribusiness sector.
