Beyond Pixels: Identifying Built-Up Features at Subpixel Level for Enhanced Satellite-Based Land Cover Mapping

Abstract

Identifying buildings and urban/built-up features has become increasingly important as cities grow more complex and traditional pixel-based classification methods struggle with mixed-land-cover signatures. Despite their high spatial resolution and rich multispectral capabilities, openly available datasets, such as Sentinel-2, often remain insufficient for extracting fine-scale urban information. For example, roads, buildings that are smaller than their spatial resolution, or urban areas alongside gardens, are the most challenging features to identify. To detect and map built-up structures that fall below the sensor's nominal pixel size, or have complex mixed land-cover signatures, we propose to use regression-based subpixel mapping. We rely on pretrained Alpha Earth Embeddings, which provide rich, multitemporal feature representations that help disentangle built-up features in mixed pixels. These embeddings are combined with freely available very high spatial resolution land-cover map for training. The proposed method could successfully identify small buildings and narrow roads, having a size smaller than the spatial resolution of the considered satellite data. In particular, it was able to detect buildings with areas as small as <bold><inline-formula><tex-math notation="LaTeX">$20 \,\,\mathrm{m}^{2}\,$</tex-math></inline-formula></bold> and roads represented by buffers as narrow as <bold><inline-formula><tex-math notation="LaTeX">$1.5 \,\mathrm{m}$</tex-math></inline-formula></bold>. Our results indicate that combining subpixel mapping with embedding representations enables improved identification of complex built-up features having a size smaller than <bold><inline-formula><tex-math notation="LaTeX">$10 \,\mathrm{m}$</tex-math></inline-formula></bold> when compared to existing land-cover maps.