Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification

This paper proposes a framework to fuse multi-seasonal Sentinel-2 images, with application on LCZ-derived urban land cover classification. Cross-validation over a seven-city study area in central Europe demonstrates its consistently better performance over several previous approaches, with the same experimental setup. Based on our previous work, we can conclude that decision-level fusion is better than feature-level fusion for similar tasks at similar scale with multi-seasonal Sentinel-2 images. With the framework, urban land cover maps of several cities are produced. The visualization of two exemplary areas shows urban structures that are consistent with existing datasets. This framework can be also generally beneficial for other types of urban mapping.