Mapping urban built-up types from 2000 to 2022 at 10-m resolution using super-resolution of Landsat spectral-temporal metrics and center-patch classification

Abstract

Detailed information on urban development types, e.g. residential, industrial, or transportation infrastructure, is essential for assessing urban growth rates, population dynamics, and environmental impacts. Earth observation imagery from Landsat and Sentinel-2 provides valuable data for characterizing urban areas and their development over large spatial extents and long temporal scales. However, mapping large areas over multiple decades poses several challenges. Specifically, the coarse resolution of historical Landsat data (30-m) limits the capacity to capture the spatial detail of diverse built-up types. Furthermore, existing mapping techniques—such as pixel-based, scene-based, and semantic segmentation - often face limitations in capturing spatial context, compromise mapping resolution, or rely on hand-crafted training data. To address these challenges, this study proposes a novel workflow comprising two key components: (1) enhancing historical Landsat spatial resolution to 10-m using a generative super-resolution model, with a focus on synthetic images derived from spectral-temporal metrics, and (2) a “center-patch classification” method, wherein patch images serve as input for the central pixel classification. We applied the proposed methodologies to produce tri-annual maps of urban built-up classes—including Residential buildings, Industrial buildings, Open spaces, and Non built-up—across the Baltic Sea region for two decades (2000−2021). Evaluation of the baseline performance demonstrated that the 10-m maps derived from super-resolution Landsat data achieved higher accuracy than existing Sentinel-2-based products. Furthermore, when applied to earlier years within the study period, the super-resolution Landsat data consistently exhibited improved classification accuracy across all urban classes compared to native-resolution Landsat data.