SPATIAL DATA QUALITY EVALUATION FOR LAND COVER CLASSIFICATION APPROACHES

Data gaps and poor data quality may lead to flawed conclusions and data-driven policies and decisions, such as the measurement of Sustainable Development Goals progress. This is particularly important for land cover data, as an essential source of data for a wide range of applications and real-world challenges including climate change mitigation, food security planning, resource allocation and mobilization. While global land cover datasets are available, their usability is limited by their coarse spatial and temporal resolutions. Furthermore, having a good understanding of the fitness for the purpose is imperative. This paper compares two datasets from a spatial data quality perspective: (1) a global land cover map, and (2) a fit-for-purpose training dataset that is generated using visual inspection of very high-resolution satellite data. The latter dataset is created using Google Earth Engine (GEE), a cloud-based computing platform and data repository. We systematically evaluate the two datasets from spatial data quality (SDQ) perspective using the Analytic Hierarchy Process (AHP) to prioritise the criteria, i.e. SDQ. To validate the results, land cover classifications are conducted using both datasets, also within GEE. Based on the results of the SDQ evaluation and land cover classification, we find that the second training dataset significantly outperformed the global land cover maps. Our study also shows that cloud-based computing platforms and publicly available data repositories can provide an effective approach to filling land cover data gaps in data-scarce regions.