Time-series land cover change detection using deep learning-based temporal semantic segmentation

The process of sustainable urban development is accompanied by frequent and complex land cover changes, and thus, clarify accurate information on land cover changes can provide scientific data for urban management. To characterize urban development at an accurate spatiotemporal scale, a change detection model is not only required to provide accurate location (Where) and time (When) of the changes, but also semantic information on the change types (What). Accordingly, this study proposed a deep learning method for temporal semantic segmentation change detection (TSSCD) that obtains information on the where, when, and what of changes simultaneously. TSSCD model bridges the semantic gap between remote sensing time series abrupt changes and land cover changes by learning the month-to-month mapping from spectral information to land cover types. We implemented a temporal semantic segmentation model based on the most classic fully convolutional network, where all two-dimensional convolutions and pooling operations were replaced with one-dimensional. We conducted tests on the TSSCD in several urban study areas, and it consistently exhibited good accuracy. In most cases, it outperformed the BFAST and CCDC algorithms, except when only a single spectral band was used. Simultaneously, we analyzed the minimum data requirements for training a TSSCD. The TSSCD currently faces challenges in achieving strong generalization beyond the training data distribution. Additionally, we observed that change detection for specific land cover types can be achieved through the flexible configuration of TSSCD. Finally, we explored a method for constructing datasets using existing products to minimize data annotation efforts, yielding promising results. However, there is still some gap compared to complete manual annotation. Overall, the TSSCD model provided a novel solution to accurately characterize sustainable urban development at the spatiotemporal scale.