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.
A description is given of the COOMET project 473/RU-a/09: a pilot comparison of hydrophone calibrations at frequencies from 250 Hz to 200 kHz between Hangzhou Applied Acoustics Research Institute (HAARI, China)—pilot laboratory—and Russian National Research Institute for Physicotechnical and Radio Engineering Measurements (VNIIFTRI, Designated Institute of Russia of the CIPM MRA). Two standard hydrophones, B&K 8104 and TC 4033, were calibrated and compared to assess the current state of hydrophone calibration of HAARI (China) and Russia. Three different calibration methods were applied: a vibrating column method, a free-field reciprocity method and a comparison method. The standard facilities of each laboratory were used, and three different sound fields were applied: pressure field, free-field and reverberant field. The maximum deviation of the sensitivities of two hydrophones between the participants' results was 0.36 dB.Main text. To reach the main text of this paper, click on Final Report.The final report has been peer-reviewed and approved for publication by the CCAUV-KCWG.
The Central Plains has a long history, rich culture, unique geographical advantages, and profound cultural heritage. The occurrence of ancient cities in the Central Plains marks the formation of Chinese state-level societies. The number, size, and distribution of ancient cities have changed greatly from the late Yangshao to the Xia and Shang Dynasties, which reflects the evolution of settlement and social organization. In this study, Geographic Information System (GIS) spatial database technology was used to establish a spatiotemporal database of ancient cities in the late Yangshao, Longshan, as well as Xia and Shang Dynasties in the Central Plains. This paper uses GIS spatial analysis technology to analyze the relationship between the ancient city distribution and the geographical environment, as well as the evolution of ancient city's shapes and sizes. Furthermore, by using the method of the nearest neighbor distance and gravity center analysis, this paper discusses the agglomeration characteristics and gravity center evolution of ancient cities. The results show that: (1) Most of the ancient cities were distributed in areas below 500 m and within 3 km from the river during the time interval from the late Yangshao to Xia and Shang Dynasties; (2) The shape of the ancient cities gradually changed from circles to squares in the Central Plains, which became a unified model for the later ancient city design; (3) The sizes of the 18 ancient cities in the Yangshao period shared high similarity, with an average area of 20 hectares. The sizes of 24 ancient cities in the Longshan period increased significantly, with an average of 39 hectares. During the Xia and Shang Dynasties, there were 22 ancient cities with an average size of 340 hectares, and the grade of sizes became obvious, marking the entrance into Chinese state-level societies; (4) Cities were scattered in the decentralized pattern during the late Yangshao and Longshan periods, whereas they became agglomerative in Xia and Shang Dynasties. This reflects the evolution of the spatial scopes and social organizational forms; and (5) From the late Yangshao to Xia and Shang Dynasties, the gravity center of ancient cities moved around the Songshan Mountain from the northwest to the southeast and again to the northeast.
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.
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