Accuracy assessment of Global Human Settlement Layer (GHSL) built-up products over China.

Building a density map over large areas could provide essential information of land development intensity and settlement condition. It is crucial for supporting studies and planning of human settlement environment. The Global Human Settlement Layer (GHSL) is a comprehensive data set of mapping human settlement at a global scale, which was produced by the Joint Research Centre (JRC), European Commission. The built-up density is an important layer of GHSL data set. Currently, the validation of the GHSL built-up area products was preliminarily conducted over the United States and European countries. However, as a typical East Asian region, China is quite different from the United States, Europe, and other regions in terms of building forms and urban layouts. Therefore, it is necessary to perform an accuracy assessment of GHSL data set in Asian countries like China. With individual building footprint data of 20 typical cities in China, this paper presents our effort to validate the GHSL built-up area products. The aggregation mean and neighborhood search based algorithms are adopted for matching building footprint data and the GHSL products, through the regression analysis at per-pixel level, the building density map in raster format are generated as validation data. The accuracy index of GHSL built-up area was calculated for the study areas, and the validation methods were explored for GHSL built-up products at large scale. The results show that the built-up layer aggregated by the building footprint have the highest correlation with the coarse resolution GHSL built-up products, but GHSL tends to underestimate the building density of low-density areas and overestimate the areas with high density. This study suggests that GHSL built-up area products in 20 representative Chinese cities of China could provide quantitative information about built-up areas, but the product accuracy still need to be improved in the regions with heterogeneous formations of human settlements like China. There is a big picture of mapping high accuracy built-up density of China with the training data set acquired by the study.

Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making.

In this paper, a set of Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers (qROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of qROFNs based on the Dombi t-conorm and t-norm are introduced. A q-rung orthopair fuzzy Dombi partitioned Heronian mean (qROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q-rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining qROFDPHM operator with the power average operator. A method to solve MAGDM problems based on qROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.

Multi-criteria group decision making based on Archimedean power partitioned Muirhead mean operators of q-rung orthopair fuzzy numbers.

Two critical tasks in multi-criteria group decision making (MCGDM) are to describe criterion values and to aggregate the described information to generate a ranking of alternatives. A flexible and superior tool for the first task is q-rung orthopair fuzzy number (qROFN) and an effective tool for the second task is aggregation operator. So far, nearly thirty different aggregation operators of qROFNs have been presented. Each operator has its distinctive characteristics and can work well for specific purpose. However, there is not yet an operator which can provide desirable generality and flexibility in aggregating criterion values, dealing with the heterogeneous interrelationships among criteria, and reducing the influence of extreme criterion values. To provide such an aggregation operator, Muirhead mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are concurrently introduced into q-rung orthopair fuzzy sets, and an Archimedean power partitioned Muirhead mean operator of qROFNs and its weighted form are presented and a MCGDM method based on the weighted operator is proposed in this paper. The generalised expressions of the two operators are firstly defined. Their properties are explored and proved and their specific expressions are constructed. On the basis of the specific expressions, a method for solving the MCGDM problems based on qROFNs is then designed. Finally, the feasibility and effectiveness of the method is demonstrated via a numerical example, a set of experiments, and qualitative and quantitative comparisons.

Semantic Segmentation of Building Roof in Dense Urban Environment with Deep Convolutional Neural Network: A Case Study Using GF2 VHR Imagery in China.

This paper presents a novel approach for semantic segmentation of building roofs in dense urban environments with a Deep Convolution Neural Network (DCNN) using Chinese Very High Resolution (VHR) satellite (i.e., GF2) imagery. To provide an operational end-to-end approach for accurately mapping build roofs with feature extraction and image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform building roof segmentation. We selected typical cities with dense and diverse urban environments in different metropolitan regions of China as study areas, and sample images were collected over cities. High performance GPU-mounted workstations are employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with convolution layers is developed for building roof segmentation. The validation shows that the overall accuracy (OA) and the mean Intersection Over Union (mIOU) of DCNN-based semantic segmentation results are 94.67% and 0.85, respectively, and the CRF-refined segmentation results achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas in dense urban environments with different building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline of operational built-up area monitoring, and the timely update of building roof map could be applied in urban management and assessment of human settlement-related sustainable development goals over large areas.

An index of non-sampling error in area frame sampling based on remote sensing data.

Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units.

Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping.

Urban land cover map is essential for urban planning, environmental studies and management. This paper aims to demonstrate the potential of geometric and radiometric features derived from LiDAR waveform and point cloud data in urban land cover mapping with both parametric and non-parametric classification algorithms. Small footprint LiDAR waveform data acquired by RIEGL LMS-Q560 in Zhangye city, China is used in this study. A LiDAR processing chain is applied to perform waveform decomposition, range determination and radiometric characterization. With the synergic utilization of geometric and radiometric features derived from LiDAR data, urban land cover classification is then conducted using the Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and random forest algorithms. The results suggest that the random forest classifier achieved the most accurate result with overall classification accuracy of 91.82% and the kappa coefficient of 0.88. The overall accuracies of MLC and SVM are 84.02, and 88.48, respectively. The study suggest that the synergic utilization of geometric and radiometric features derived from LiDAR data can be efficiently used for urban land cover mapping, the non-parametric random forest classifier is a promising approach for the various features with different physical meanings.

Range determination for generating point clouds from airborne small footprint LiDAR waveforms.

This paper presents a range determination approach for generating point clouds from small footprint LiDAR waveforms. Waveform deformation over complex terrain area is simulated using convolution. Drift of the peak center position is analyzed to identify the first echo returned by the illuminated objects in the LiDAR footprint. An approximate start point of peak in the waveform is estimated and adopted as the indicator of range calculation; range correction method is proposed to correct pulse widening over complex terrain surface. The experiment was carried out on small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. The results suggest that the proposed approach generates more points than standard commercial products; based on field measurements, a comparative analysis between the point clouds generated by the proposed approach and the commercial software GeocodeWF indicates that: 1). the proposed approach obtained more accurate tree heights; 2). smooth surface can be achieved with low standard deviation. In summary, the proposed approach provides a satisfactory solution for range determination in estimating 3D coordinate values of point clouds, especially for correcting range information of waveforms containing deformed peaks.