Source-free Multi-target Unsupervised Domain Adaptation for Cross-City Local Climate Zone Classification

Local Climate Zones (LCZs) offer a standardized urban classification system critical for studying climate variations and the urban heat island effect. Large-scale LCZ mapping facilitates cross-city climate comparisons. Remote sensing (RS), particularly supervised learning, has become the primary LCZ classification method due to satellite imagery’s broad coverage and high resolution. However, current RS approaches face challenges, including high labeling demands and limited transferability. In this work, we aim to leverage limited labeled data from a source city ( i.e. , source domain) to improve LCZ classification performance for multiple unlabeled target cities ( i.e. , target domains). To address these challenges, this work proposes a novel Source-free Multi-target Unsupervised Domain Adaptation (SFMT-UDA) framework for cross-city LCZ classification. Our approach operates in two stages: first performing single-target adaptation between source and target domains using a self-supervised pseudo-labeling module enhanced by an LCZ class similarity matrix to reduce label noise, then conducting multi-target adaptation among target domains through a Multi-head Multi-target Domain Adaptation (MH-MTDA) network with an integrated style-transfer module for consistent self-training. Extensive experiments on the newly developed VHRLCZ dataset demonstrate that the proposed SFMT-UDA framework achieves superior performance compared to state-of-the-art methods, showing significant improvements in overall accuracy across multiple target domains. The related code and data are available at https://github.com/ctrlovefly/SFMT-UDA.