Abstract
High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features of HRBs, it is still challenging to monitor large-scale HRBs in a routine way. This paper extends our previous work on the use of the Fully Convolutional Networks (FCN) model to extract HRBs from Sentinel-2 data by studying the influence of seasonal and spatial factors on the performance of the FCN model. 16 Sentinel-2 subset images covering four diverse regions in four seasons were selected for training and validation. Our results indicate the performance of the FCN-based method at the extraction of HRBs from Sentinel-2 data fluctuates among seasons and regions. The seasonal change of accuracy is larger than that of the regional change. If an optimal season can be chosen to get a yearly best result, F1 score of detected HRBs can reach above 0.75 for all regions with most errors located on the boundary of HRBs. FCN model can be trained on seasonally and regionally combined samples to achieve similar or even better overall accuracy than that of the model trained on an optimal combination of season and region. Uncertainties exist on the boundary of detected results and may be relieved by revising the definition of HRBs in a more rigorous way. On the whole, the FCN based method can be largely effective at the extraction of HRBs from Sentinel-2 data in regions with a large diversity in culture, latitude, and landscape. Our results support the possibility to build a powerful FCN model on a larger size of training samples for operational monitoring HRBs at the regional level or even on a country scale.
Highlights
With decades of rapid urbanization, High-Rise Buildings (HRBs) have been emerging as a distinctive landscape in urban areas in China
The accuracies of HRB detection results differ in four regions, taking seasonally average accuracy of HRB detection results as the criteria, Guangzhou is about 0.55, and it is the worst compared to others
For convenience of read, we refer to the Fully Convolutional Networks (FCN) model trained on samples from a specific combination of season and region as the single season model, and the FCN model trained on seasonally combined data from a specific region as the all seasons model
Summary
With decades of rapid urbanization, High-Rise Buildings (HRBs) have been emerging as a distinctive landscape in urban areas in China. HRBs mainly serving as high-end commercial and business centers and residential apartments have obvious advantages at improving the efficiency of resources and energy [1]. With their unique characteristics and functions, HRBs have a great impact on the urban environment and socioeconomics [2,3,4]. The monitoring of HRBs in urban areas can be useful in urban planning, environment protection, and ecological assessment, and so on.
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