Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Abstract

The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology.

A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks

“…In these examples, it is expected that the globally available multi-spectral Sentinel-2 data, with a 5-day temporal resolution and 10-meter spatial resolution, are going to play a key role in more accurate HSE mapping at a large or even global scale, with the potential for frequent monitoring of global urbanization. This is already being shown by some regional-scale studies, with similar applications on urban impervious surface mapping [16] and land cover mapping [9,17].…”

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

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“…Furthermore, for areas that are affected by seasonal land cover changes, we assume intra-seasonal changes to be less significant than inter-seasonal changes. Last, but not least, there have been first hints in the literature that a fusion of multi-seasonal information can already provide a useful information gain for land cover classification approaches (Qiu et al, 2019). (1) areas that are rarely covered by clouds, (2) areas that are moderately covered by clouds, (3) areas that are frequently covered by clouds, and (4) areas that are almost always covered by clouds.…”