Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China’s major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China’s major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R² models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China’s major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Poverty has always been a global concern that has restricted human development. The first goal (SDG 1) of the United Nations Sustainable Development Goals (SDGs) is to eliminate all forms of poverty all over the world. The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1. This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework. The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012, 2014, 2016, and 2018 have been analyzed. Based on the SDGs global indicator framework, this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images, land cover data, and digital elevation model data. Secondly, we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model. Finally, we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values. The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties. The estimated model R² is 0.65, which can identify 72.77% of China’s national poverty counties. From 2012 to 2018, the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China. In addition, the average SDG 1 evaluation value of China’s districts and counties increased by 23% from 2012 to 2018. This paper is oriented to the United Nations SDGs framework, explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data, and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM_2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM_2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM_2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM_2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM_2.5 concentration is inverted. The results show that the PM_2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM_2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM_2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM_2.5 concentration estimation and prediction based on satellite remote sensing technology.

The classification and extraction of street tree geometry information in road scenes is crucial in urban forest biomass statistics and road safety. To address the problem of 3D fine extraction of street trees in complex road scenes, this paper designs and investigates a method for extracting street tree geometry and forest parameters from vehicle-mounted LiDAR point clouds in road scenes based on a Gaussian distributed regional growth algorithm and Voronoi range constraints. Firstly, a large number of non-tree and other noise points, such as ground points, buildings, shrubs and vehicle points, are filtered by applying multi-geometric features; then, the main trunk of the street tree is extracted based on the vertical linear features of the tree and the region growth algorithm based on Gaussian distribution; secondly, a Voronoi polygon constraint is established to segment the single tree canopy region with the main trunk center of mass; finally, based on the extracted locations of the street trees and their 3D points, the tree growth parameters of individual trees are obtained for informative management and biomass estimation by combining geometric statistical methods. In this paper, the experimental data from vehicle-borne LiDAR point clouds of different typical areas were selected to verify that the proposed Gaussian-distributed regional growth algorithm can achieve fine classification and extraction of tree growth parameters for different types of roadside trees, with accuracy, recall and F1 values reaching 96.34%, 97.22% and 96.45%, respectively. This research method can be used for the extraction of 3D fine classification of street trees in complex road environments, which in turn can provide support for the safety management of traffic facilities and forest biomass estimation in urban environments.

Geographical object classification and information extraction is an important topic for the construction of 3D virtual reality and digital twin cities in urban areas. However, the majority of current multi-target classification of urban scenes uses only a single source data (optical remote sensing images or airborne laser scanning (ALS) point clouds), which is limited by the restricted information of the data source itself. In order to make full use of the information carried by multiple data sources, we often need to set more parameters as well as algorithmic steps. To address the above issues, we compared and analyzed the object classification methods based on data fusion of airborne LiDAR point clouds and optical remote sensing images, systematically. Firstly, the features were extracted and determined from airborne LiDAR point clouds and high-resolution optical images. Then, some key feature sets were selected and were composed of median absolute deviation of elevation, normalized elevation values, texture features, normal vectors, etc. The feature sets were fed into various classifiers, such as random forest (RF), decision tree (DT), and support vector machines (SVM). Thirdly, the suitable feature sets with appropriate dimensionality were composed, and the point clouds were classified into four categories, such as trees (Tr), houses and buildings (Ho), low-growing vegetation (Gr), and impervious surfaces (Is). Finally, the single data source and multiple data sources, the crucial feature sets and their roles, and the resultant accuracy of different classifier models were compared and analyzed. Under the conditions of different experimental regions, sampling proportion parameters and machine learning models, the results showed that: (1) the overall classification accuracy obtained by the feature-level data fusion method was 76.2% compared with the results of only a single data source, which could improve the overall classification accuracy by more than 2%; (2) the accuracy of the four classes in the urban scenes can reach 88.5% (Is), 76.7% (Gr), 87.2% (Tr), and 88.3% (Ho), respectively, while the overall classification accuracy can reach 87.6% with optimal sampling parameters and random forest classifiers; (3) the RF classifier outperforms DT and SVM for the same sample conditions. In this paper, the method based on ALS point clouds and image data fusion can accurately classify multiple targets in urban scenes, which can provide technical support for 3D scene reconstruction and digital twin cities in complex geospatial environments.