Denoising of Binary Built-Up Maps Using Multi-Temporal Image Processing Thresholding

Accurate identification of built-up land from remotely sensed imagery is essential for urban planning, environmental monitoring, and disaster response. However, binary built-up maps derived from single-date classifications often contain semantic noise—misclassified pixels resulting from shadows, bare soil confusion, or seasonal conditions. Common denoising methodologies, such as smoothing or filtering, are designed for continuous imagery and can distort small or fragmented features and fail to correct underlying classification errors. To overcome these limitations, this study evaluated a multi-date summation and thresholding workflow as a denoising alternative. Five Sentinel-2 images per site were classified as built-up maps, summed into a composite “built-up frequency” raster, and thresholded using Otsu, adaptive, and voting methods to produce refined binary maps. The results across nine international study sites show that the Otsu thresholding method outperformed the other methods in most locations when comparing their accuracies using the Matthews Correlation Coefficient (MCC), showing that using multiple images can improve identification of built-up land.