Enabling Advanced Land Cover Analytics: An Integrated Data Extraction Pipeline for Predictive Modeling with the Dynamic World Dataset
This work addresses the problem of high learning curves and lack of standardization in land cover data processing for researchers, though it is incremental as it builds on existing datasets and methods.
The study tackled the challenge of accessing and processing the Dynamic World land cover dataset by developing an end-to-end pipeline that democratizes data use, resulting in excellent performance for urbanization prediction models.
Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. Nevertheless, although the data is accessible to any community member interested in exploration, there exists a formidable learning curve and no standardized process for accessing, pre-processing, and leveraging the data for subsequent tasks. In this study, we democratize this data by presenting a flexible and efficient end to end pipeline for working with the Dynamic World dataset, a cutting-edge near-real-time land use/land cover (LULC) dataset. This includes a pre-processing and representation framework which tackles noise removal, efficient extraction of large amounts of data, and re-representation of LULC data in a format well suited for several downstream tasks. To demonstrate the power of our pipeline, we use it to extract data for an urbanization prediction problem and build a suite of machine learning models with excellent performance. This task is easily generalizable to the prediction of any type of land cover and our pipeline is also compatible with a series of other downstream tasks.