Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

A New Look at Building Facades as Infrastructure

Like the hard surfaces of streets and sidewalks in an urban environment, the vertical and horizontal surface area on the outside of urban buildings contributes to the constant heating of large cities around the world. However, little is done to design this surface to benefit the public sphere. Instead, the facade of a building performs either as a component that focuses only on the quality of comfort for interior occupants, while ignoring effects on the exterior of the building, or as an identifiable aesthetic for the building＇s owners, This essay proposes the rethinking of the building facade as a steward of outdoor pedestrian welfare, and the conception of public health as an added function of the building envelope- a concept that may fall into the jurisdiction of public works. If the huge total surface area of a city＇s buildings is thought of as part of the city＇s infrastructure, then its public contribution may not only make outdoor areas comfortable, clean, and enjoyable, but also help to alleviate the bigger problem of rising temperatures in cities.

Construction of Green Ecological Corridor and Vertical Greening of Building Facades in Zhengzhou City

Development of a city depends largely on its transportation, and road construction reveals development skeletons of the city. With the aggravation of city pollution, construction of ecological corridor has been put on the agenda of urban ecological construction, against this background, "2 rings and 17 radial lines" of Zhengzhou City were planned. This paper analyzed the plan from the perspectives of role of urban green corridor, planning principle, characteristic building and greening of building facade, and exploration of green corridors integrating pathways, bikeways, bus harbors and green island gas station.

Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net

The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades,as buildings contribute significantly to energy consumption in urban environments.However,conventional image segmentation methods often struggle to capture fine details such as edges and contours,limiting their effectiveness in identifying areas prone to energy loss.To address this challenge,we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model,Cascade U-Net.Object-wise processing isolates components of the facade,such as walls and windows,for independent analysis,while Cascade U-Net incorporates contour information to enhance segmentation accuracy.The methodology involves four steps:object isolation,which crops and adjusts the image based on bounding boxes;contour extraction,which derives contours;image segmentation,which modifies and reuses contours as guide data in Cascade U-Net to segment areas;and segmentation synthesis,which integrates the results obtained for each object to produce the final segmentation map.Applied to a dataset of Korean building images,the proposed method significantly outperformed traditional models,demonstrating improved accuracy and the ability to preserve critical structural details.Furthermore,we applied this approach to classify window thermal loss in real-world scenarios using infrared images,showing its potential to identify windows vulnerable to energy loss.Notably,our Cascade U-Net,which builds upon the relatively lightweight U-Net architecture,also exhibited strong performance,reinforcing the practical value of this method.Our approach offers a practical solution for enhancing energy efficiency in buildings by providing more precise segmentation results.

Solar Collectors of Buildings Facade Based on Aluminum Heat Pipes with Colored Coating

A variety of liquid thermal solar collectors designs used for water heating have been developed by the previous researchers. But the majority of them do not meet the requirements on small weight, easy assembling and installing, versatility, scalability and adaptability of the design, which are particularly important when they are facade integrated. In order to avoid the above mentioned drawbacks of the liquid thermal collectors, the authors propose to apply to them extruded aluminum alloy made heat pipes of originally designed cross-sectional profile with wide fins and longitudinal grooves. Such solar collectors could be a good solution for building facade and roof integration, because they are assembled of several standard and independent, hermetically sealed and light-weight modules, easy mounted and ＂dry＂ connected to the main pipeline. At that, their thermal performances are not worse than of the other known ones made of heavier and more expensive copper with higher thermal conductance, or having entire rigid designs. Some variants of the developed solar collectors shaping of the assembled modules for building facade or roof integration are proposed. Variously colored coatings to the absorbers are developed and made of carbon-siliceous nano-composites by means of sol-gel method. Their optical performances were compared with ＂anodized black＂. It is stated that colored coatings have a good prospect in thermal SCs （solar collectors） adaptation to building facades decoration, but the works on study and upgrade of their performances should be continued.

Deep learning for estimating energy savings of early-stage facade design decisions

The selection of high-performance building facade systems is essential to promote building energy efficiency.However,this selection is highly dependent on early-stage design decisions,which are extremely challenging considering numerous design parameters with early-stage uncertainties.This paper aims to evaluate the appli-cability of deep learning networks in estimating the energy savings of different facade alternatives in the early-stage design of buildings.The energy performance of two competing façade systems(i.e.,Ultra-High-Performance Fiber-Reinforced-Concrete and conventional panels)was estimated for different scenarios through building en-ergy simulations using EnergyPlusTM.Three deep learning networks were trained using the collected data from the simulation of fourteen buildings in fourteen different locations to estimate the heating,cooling,and total site energy savings.The accuracy of trained deep networks was compared with the accuracy of three common data-driven prediction models including,Gradient Boosting Machines,Random Forest,and Generalized Linear Regression.The results showed that the deep learning network trained to predict building total site energy savings had the highest accuracy among other models with a mean absolute error of 1.59 and a root mean square error of 3.48,followed by Gradient Boosting Machines,Random Forest,and last Generalized Linear Regression.Similarly,deep networks trained to predict building cooling and heating energy savings had the lowest mean average error of 0.20 and 1.17,respectively,compared to other predictive models.It is expected the decision support system developed based on this methodology helps architects and designers to quantify the energy savings of different facade systems in early stages of design decisions.

Sub-Atmospheric Pressure Coupled with Width Effect on Downward Flame Spread over Energy Conservation Material Polyurethane Foam

Rigid polyurethane(PUR)foam,a sustainable thermosetting building facade porous polymer material,has been widely applied in the construction industry for energy conservation.Additional knowledge of the fire safety performance of PUR foam at different altitudes and sample widths is required.Comparative lab-scale experiments were conducted in the Lhasa plateau(66.5 kPa)and the Hefei plain(99.8 kPa)in China.Flame propagation characteristics(average flame spread rate and flame height)were measured at different widths and atmospheric pressures of the test locations.Experimental results show that the dependence of dimensionless flame heights on sample width shows negative power law relationships with index of−w/5.4 to−w/5.8.Both flame height and flame spread rate were lower under low ambient pressure conditions as H f P0.26~0.33 and Vf P0.057~0.568.Flame spread rate decreased with increasing sample width in the convection regime before a critical width of 4 cm–8 cm,after which the flame spread rate increased in the radiation regime.Results of this study contribute to the science of combustion,fire safety and energy conservation,and provide a basis for fire safety protocols for historical heritage buildings in the Lhasa plateau.