Research Project (Ongoing)

Geo-AI for Bridging SDG Environmental Indicator 6.6.1 Data Gaps in Developing Countries: A Case Study of Indonesia

Many environmental SDG indicators, particularly in developing countries, suffer from incomplete or missing data. This also applies to SDG 6 (Ensure availability and sustainable management of water and sanitation for all), and specifically indicator 6.6.1 (Change in the extent of water-related ecosystems over time), due to limited monitoring networks, high costs of data collection, challenging geography, frequent cloud cover, and weak environmental information systems. As a result, countries such as Indonesia rely on proxy data, like land use change, instead of the detailed ecosystem-specific data required by UN SDG metadata. This limits effective SDG monitoring and evidence-based policy making.

The study proposes geospatial artificial intelligence (Geo-AI)—which integrates satellite remote sensing, machine learning, spatial analysis, and cloud computing—as a solution to bridge these data gaps by producing high-resolution, temporally consistent environmental information. Indonesia is used as a case study because it faces major SDG reporting challenges while also having strong potential for Geo-AI applications.