Leveraging the use of labeled benchmark datasets for urban area change mapping and area estimation: a case study of the Washington DC–Baltimore region

Abstract

Worldwide economic development and population growth have led to unprecedented changes in urban land use in the twenty-first century. As satellite data become available at higher spatial (3–10 m) and temporal (1–3 days) resolution, new opportunities arise to map and quantify urban area changes. While deep learning (DL) models have recently shown great performance when dealing with satellite data, their training requires a lot of labeled data which are not necessarily available at global scale. Satellite benchmark datasets, commonly used to advance methods, provide labeled data, but are rarely used for mapping and area estimation outside the training data. In this study, we aim to utilize the Sentinel-2-based benchmark dataset, Onera Satellite Change Detection (OSCD), to train a DL model and analyze its performance at local scale to map urban land use changes, estimate area of changes and provide characterization of changes. We apply the model over the Washington DC–Baltimore area for 2018–2019. We show that in just one year almost 1% of the total urban area underwent changes with the majority coming from the construction of commercial buildings, followed by residential buildings. Almost 10% of changes were attributed to the construction of new or renovation of existing schools.

Keywords

• sentinel-2
• urban area
• change detection
• deep learning