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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]

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs. [ABSTRACT FROM AUTHOR]

Our society's growing need for mineral resources brings with it the associated risk of degrading our natural environment as well as impacting on neighboring communities. To better manage this risk, especially for open-pit mine (OM) operations, new earth observation tools are required for more accurate baseline mapping and subsequent monitoring. The purpose of this paper is to propose an object-oriented open-pit mine mapping (OOMM) framework from Gaofen-2 (GF-2) high-spatial resolution satellite image (HSRSI), based on convolutional neural networks (CNNs). To better present the different land use categories (LUCs) in the OM area, a minimum heterogeneity criterion-based multi-scale segmentation method was used, while a mean area ratio method was applied to optimize the segmentation scale of each LUC. After image segmentation, three object-feature domains were obtained based on the GF-2 HSRSI: spectral, texture, and geometric features. Then, the gradient boosting decision tree and Pearson correlation coefficient were used as an object feature information reduction (FIR) method to recognize the distinguishing feature that describe open-pit mines (OMs). Finally, the CNN was used by combing the significant features to map the OM. In total, 105 OM sites were extracted from the interpretation of GF-2 HSRSIs and the boundary of each OM was validated by field work and used as inputs to evaluate the open-pit mine mapping (OMM) accuracy. The results revealed that: (1) the FIR tool made a positive impact on effective OMM; (2) by splitting the segmented objects into two groups, training and testing sets which are composed of 70% of the objects, and validation sets which are formed by the remaining 30% of the objects, then combing the selected feature subsets for training to achieve an overall accuracy (OA) of 90.13% and a Kappa coefficient (KC) of 0.88 of the whole datasets; (3) comparing the results of the state-of-the-art method, support vector machine (SVM), in OMM, the proposed framework outperformed SVM by more than 7.28% in OA, 8.64% in KC, 6.15% in producer accuracy of OM and by 9.31% in user accuracy of OM. To the best of our knowledge, it is the first time that OM information has been used through the integration of multiscale segmentation of HSRSI with the CNN to get OMM results. The proposed framework can not only provide reliable technical support for the scientific management and environmental monitoring of open pit mining areas, but also be of wide generality and be applicable to other kinds of land use mapping in mining areas using HSR images. [ABSTRACT FROM AUTHOR]