Fully automatic training sample collection for detecting multi-decadal inland/seaward urban sprawl

Accurate and timely monitoring of urban sprawl using remote sensing provides valuable insights for urban management and sustainable development. While existing studies have explored methods for large-scale monitoring of urban sprawl, most of them rely on the collection of manually labeled training samples for supervised classification. Additionally, previous studies generally focused on inland urban sprawl, neglecting the process of urban sprawl towards sea areas, known as seaward urban sprawl, which has profound ecological implications for coastal ecosystems. Here we develop a fully automatic algorithm for detecting urban sprawl, named Fully Automatic Detection of Urban Sprawl (FADUS), using the time-series Landsat imagery without manually collecting training samples or existing urban maps for reference. FADUS automatically generates initial samples through a sequential binary unsupervised clustering technique and then updates them by removing the spectral outliers to obtain a high-quality training set for subsequent supervised classification. A tidal module is included in FADUS to avoid pseudo-urban changes caused by tidal fluctuations so that both inland and seaward urban sprawl can be detected. By applying the algorithm to 75 coastal cities in China, the most rapidly urbanizing region in the world, we uncovered a neglected but dramatic seaward urban sprawl process that has converted sea areas to urban space since 1985. Our results indicate that 9904.36 km2 of coastal wetlands and nearshore seawater areas have been converted to land area, of which 44.83% is currently covered by built-up area. With our method, the fully automatic detection of urban dynamics is now possible for both coastal and inland cities worldwide. Our derived map of seaward urban sprawl in China provides valuable references for coastal land-cover change monitoring, wetland protection and restoration, and integrated coastal management.