Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

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.

Ecology and morphology of the dwarf bromeliad boa Ungaliophis panamensis (Squamata, Boidae, Ungaliophiinae) in Costa Rica and Panama

Ecological and morphological data on Ungaliophis panamensis is extremely limited as this species is rarely encountered. These knowledge gaps have been advanced in this study where data was analysed from a small sample of snakes collected in two tropical forested environments in Costa Rica and Panama. Standardised major axis testing and a Bayesian latent variable ordination revealed that the species is sexually dimorphic, closely associated with tree trunks in natural forested areas, and occasionally discovered in rural buildings. Although further investigation into its natural history is warranted, this study shows that even with just a few individuals it is possible to elucidate ecological information that is relevant to the conservation of snake species.

Typical brick masonry walls reinforced with high-strength mortar and steel bars in the horizontal joints

Based on investigation of rural buildings in china, there are more than 20% of the masonry structures constructed in 1970s. Thus, the old blue bricks (OBB) and old red bricks (ORB), which demolished from the typical brick masonry structures was built in 1970s, was chosen in the test. During demolishing the OBB and ORB, the original mortar was destroyed. Thus, the 1:7.8 cement mortar was chosen instead of original mortars and the 1:5 cement mortar was chosen as the reinforcement mortar. In order to know the performant of the reinforcement methods, there are three-level test plan was put forward in the study. Firstly, the mechanical properties of OBB and ORB and mortars was tested; Secondly, the experiment tested the shear strength of the reinforced and unreinforced masonry specimens along mortar joints; Thirdly, there are four walls (OBB reinforced wall and unreinforced wall, ORB reinforced wall and unreinforced wall) have been made for the pseudo-static tests. This research conducted physical performance tests on masonry bricks, masonry components, and masonry walls of typical masonry structures. Through experiments, the shear capacity of the masonry structure reinforced by high-strength mortar and steel bars can be obtained.

A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings

Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named Histogram Thresholding Mask Region-Based Convolutional Neural Network (HTMask R-CNN), to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework’s performance with increasing training data and found that it converged even when the training samples were limited. This framework’s main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China’s new and old rural buildings viable.

Comparative analysis of low-energy buildings to help achieve carbon neutrality technology

The development of low-energy buildings is an important initiative to achieve carbon peaking by 2030 and carbon neutrality by 2060. According to the data of the relevant papers, if all the northern urban and rural buildings in China adopt passive ultra low energy building technology, it can save about 350 million tons of coal for heating and reduce about 900 million tons of carbon dioxide emissions each year. It’s of great significance to achieve the goals of “peak carbon dioxide emissions” and “carbon neutrality”. Starting from four key technologies for low-energy buildings, explanation and analysis the energy-saving methods for low-energy buildings, It also presents the challenges and suggestions for the development of low-energy buildings in China.