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Gross floor area is defined as the product of number of building stories and its base area. Gross floor area acquisition is the core problem to estimate floor area ratio, which is an important indicator for many geographical analyses. High data acquisition cost or inherent defect of methods for existing gross floor area acquisition methods limit their applications in a wide range. In this paper we proposed three instance-wise gross floor area estimation methods in various degrees of end-to-end learning from monocular optical images based on the NoS R-CNN, which is a deep convolutional neural network to estimate the number of building stories. To the best of our knowledge, this is the first attempt to estimate instance-wise gross floor area from monocular optical satellite images. For comparing the performance of the proposed three methods, experiments on our dataset from nine cities in China were carried out, and the results were analyzed in detail in order to explore the reasons for the performance gap between the different methods. The results show that there is an inverse relationship between the model performance and the degree of end-to-end learning for base area estimation task and gross floor area estimation task. The quantitative and qualitative evaluations of the proposed methods indicate that the performances of proposed methods for accurate GFA estimation are promising for potential applications using large-scale remote sensing images. The proposed methods provide a new perspective for gross floor area/floor area ratio estimation and downstream tasks such as population estimation, living conditions assessment, etc. [ABSTRACT FROM AUTHOR]

Stereo photogrammetric survey used to be used to extract the height of buildings, then to convert the height to number of stories through certain rules to estimate the number of stories of buildings by means of satellite remote sensing. In contrast, we propose a new method using deep learning to estimate the number of stories of buildings from monocular optical satellite image end to end in this paper. To the best of our knowledge, this is the first attempt to directly estimate the number of stories of buildings from monocular satellite images. Specifically, in the proposed method, we extend a classic object detection network, i.e., Mask R-CNN, by adding a new head to predict the number of stories of detected buildings from satellite images. GF-2 images from nine cities in China are used to validate the effectiveness of the proposed methods. The result of experiment show that the mean absolute error of prediction on buildings whose stories between 1–7, 8–20, and above 20 are 1.329, 3.546, and 8.317, respectively, which indicate that our method has possible application potentials in low-rise buildings, but the accuracy in middle-rise and high-rise buildings needs to be further improved. [ABSTRACT FROM AUTHOR]