The need for accurate land use data has never been greater as sustainability, climate resilience, and responsible agriculture gain global attention. In Ghana's cocoa-growing regions, a groundbreaking dataset developed by the Centre for Remote Sensing and Geographic Information Services (CERSGIS) is helping bridge this gap Mapping Cocoa Landscape.

The dataset contains 21,031 geocoded cocoa farm polygons, alongside more than 20,000 reference points covering other land uses such as degraded forests, oil palm plantations, rubber farms, and informal mining sites. Collected using advanced tools, including OpenForis Ground, Collect Earth Online, and KoboToolbox, the data provides a reliable foundation for training and validating machine learning models.

High-quality reference data is essential for improving the accuracy of remote sensing and AI-powered land use classification. With this dataset, researchers, policymakers, and sustainability practitioners can better:

Beyond environmental monitoring, the inclusion of anonymized household survey data offers valuable insights into the socioeconomic factors influencing land use decisions, creating opportunities for more informed and inclusive policy interventions.

The dataset also highlights the growing role of artificial intelligence in agriculture. By combining field-verified data with satellite imagery, AI models can identify land use changes faster, more accurately, and at a much larger scale than traditional mapping approaches.

As Ghana continues its digital transformation journey, initiatives like this demonstrate how geospatial technologies and AI can drive smarter decision-making, support sustainable cocoa production, and strengthen environmental stewardship.

The future of sustainable agriculture will be shaped by data. This dataset is a significant step toward building that future.