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High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding. However, few datasets can be used for land-use/land-cover (LULC) classification, binary change detection (BCD) and semantic change detection (SCD) simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location, ignoring the changed classes. Public SCD datasets are rare but much needed. To solve the above problems, a tri-temporal SCD dataset made up of Gaofen-2 (GF-2) remote sensing imagery (with 11 LULC classes and 60 change directions) was built in this study, namely, the Wuhan Urban Semantic Understanding (WUSU) dataset. Popular deep learning based methods for LULC classification, BCD and SCD are tested to verify the reliability of WUSU. A Siamese-based multi-task joint framework with a multi-task joint loss (MJ loss) named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification, BCD and SCD, compared to the state-of-the-art (SOTA) methods. Finally, a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUSU dataset and the ChangeMJ framework have good application values.

Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery: from tri-temporal datasets to multi-task mapping