Abstract
The major part of the population lives in urban areas, and this is expected to increase in the future. The main challenges faced by cities currently and towards the future are the rapid urbanization, the increase in urban temperature and the urban heat island. Mapping and monitoring urban fabric (UF) to analyze the environmental impact of these phenomena is more necessary than ever. This coupled with the increased availability of Earth observation data and their growing temporal capabilities leads us to consider using temporal features for improving land use classification, especially in urban environments where the spectral overlap between classes makes it challenging. Urban land use classification thus remains a central question in remote sensing. Although some research studies have successfully used multi-temporal images such as Landsat-8 or Sentinel-2 to improve land cover classification, urban land use mapping is rarely carried using the temporal dimension. This paper explores the use of Sentinel-2 data in a deep learning framework, by firstly assessing the temporal robustness of four popular fully convolutional neural networks (FCNs) trained over single-date images for the classification of the urban footprint, and secondly, by proposing a multi-temporal FCN. A performance comparison between the proposed framework and a regular FCN is also conducted. In this study, we consider four UF classes typical of many European Western cities. Results show that training the proposed multi-date model on Sentinel 2 multi-temporal data achieved the best results with a Kappa coefficient increase of 2.72% and 6.40%, respectively for continuous UF and industrial facilities. Although a more definitive conclusion requires further testing, first results are promising because they confirm that integrating the temporal dimension with a high spatial resolution into urban land use classification may be a valuable strategy to discriminate among several urban categories.
Highlights
In 2018, 55% of the world’s population lived in urban areas against only 30% in the 1950s
A reasonable conclusion is that learning new features using multi-temporal data and deep learning can improve the classification accuracy for both continuous urban fabrics (UF) and industrial facilities
This work explored the use of multi-temporal Sentinel-2 images for UF mapping
Summary
In 2018, 55% of the world’s population lived in urban areas against only 30% in the 1950s This value is expected to reach 68% in 2050 [1]. This growing urbanization makes urban areas very dynamic and changes the way people live, consume and exploit resources. For many years, it was monitored through mapping the urban footprint, which includes the road network, buildings, vegetation, and impervious surfaces. It was monitored through mapping the urban footprint, which includes the road network, buildings, vegetation, and impervious surfaces These elements structure the spatial layout of cities resulting in various urban fabrics (UF) [2]. Mapping UF is crucial for understanding and simulating urban dynamics [5]
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