Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor...Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor floor plan with rooms and corridors as well as landmarks, using inertial traces collected with smartphones. Landmarks,such as turnings, doors, and stairs, are identified according to inertial sensors and WiFi signals. The inertial traces are then partitioned into segments according to the turnings and classified as room type or corridor type according to the doors. Clustering is applied on room type and corridor type trace segments separately to produce room type and corridor type clusters. The construction of room applies the α-shape algorithm on room type clusters and the construction of corridor employs the principal component analysis(PCA) algorithm or the α-shape algorithm on corridor type clusters. Evaluations in two representative scenarios show that the method can construct the floor plan of acceptable accuracy with a relatively small set of inertial traces.展开更多
In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural p...In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural plan is designed in different dimensions. While designing all these modes takes considerable time, there are numerous potential applications for artificial intelligence (AI) in this domain. This study aims to compute and use an adjacency matrix to generate architectural residential plans. Additionally, it develops a plan generation algorithm in Rhinoceros software, utilizing the Grasshopper plugin to create a dataset of architectural plans. In the following step, the data was entered into a neural network to identify the architectural plan's type, furniture, icons, and use of spaces, which was achieved using YOLOv4, EfficientDet, YOLOv5, DetectoRS, and RetinaNet. The algorithm's execution, testing, and training were conducted using Darknet and PyTorch. The research dataset comprises 12,000 plans, with 70% employed in the training phase and 30% in the testing phase. The network was appropriately trained practically and precisely in relation to an average precision (AP) resulting of 91.50%. After detecting the types of space use, the main research algorithm has been designed and coded, which includes determining the adjacency matrix of architectural plan spaces in seven stages. All research processes were conducted in Python, including dataset preparation, network object detection, and adjacency matrix algorithm design. Finally, the adjacency matrix is given to the input of the proposed plan generator network, which consequently, based on the resulting adjacency, obtains different placement modes for spaces and furniture.展开更多
The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a chal...The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic materials can be significantly streamlined. In this paper, we present a novel learning framework for automatically parsing floor plan images. Our key insight is that the room type text is very common and crucial in floor plan images as it identifies the important semantic information of the corresponding room. However, this clue is rarely considered in previous learning-based methods. In contrast, we propose the Row and Column network (RC-Net) for recognizing floor plan elements by integrating the text feature. Specifically, we add the text feature branch in the network to extract text features corresponding to the room type for the guidance of room type predictions. More importantly, we formulate the Row and Column constraint module (RC constraint module) to share and constrain features across the entire row and column of the feature maps to ensure that only one type is predicted in each room as much as possible, making the segmentation boundaries between different rooms more regular and cleaner. Extensive experiments on three benchmark datasets validate that our framework substantially outperforms other state-of-the-art approaches in terms of the metrics of FWIoU, mACC and mIoU.展开更多
In most apptications, such as urbanism and architecture, randomty utitizing given spaces is certainty not favorable. This study proposes an expticit algorithm for utitizing the given spaces inside a rectangte with sat...In most apptications, such as urbanism and architecture, randomty utitizing given spaces is certainty not favorable. This study proposes an expticit algorithm for utitizing the given spaces inside a rectangte with satisfactory resutts. In the titerature, connectivity is not considered as a criterion for floor plan design, but it is deemed essentiat in architecture. For example, dinin8 rooms are preferabty connected to kitchens, toitets shoutd be connected to many rooms, and each bedroom should be separated from the other rooms. This paper describes adjacency among spaces and proves that the obtained rectangular floor plan is one of the best ones in terms of connectivity. An architectural and mathematicat object catted extra spaces is introduced by the proposed algorithm and is subseauenttv examined in this work.展开更多
Regulatory detailed planning is the most direct management tool for urban development and construction activities. Through theoretical deduction, this paper demonstrated the elastic relationship between the land-trans...Regulatory detailed planning is the most direct management tool for urban development and construction activities. Through theoretical deduction, this paper demonstrated the elastic relationship between the land-transferring price and the floor area ratio(FAR) of residential land, which represented the space control intensity for regulatory detailed planning. Based on the two dimensions of market regulation and fiscal replenishment, this paper also established an analytical framework for the space control intensity for regulatory detailed planning. The results showed that the space control intensity for regulatory detailed planning decreased with the expansion of urban scale and that the weaker the market regulation and the stronger the land finance dependence, the weaker the space control intensity for regulatory detailed planning. Based on this, this paper proposed that local governments should strengthen the scientificity and openness of regulatory detailed planning, institutionally resolve the predicament due to fiscal decentralization, and reduce the dependence on land transfer income.展开更多
基金supported by the Key Research and Development Projects of Sichuan Science and Technology Department under Grant No.2018GZ0464
文摘Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor floor plan with rooms and corridors as well as landmarks, using inertial traces collected with smartphones. Landmarks,such as turnings, doors, and stairs, are identified according to inertial sensors and WiFi signals. The inertial traces are then partitioned into segments according to the turnings and classified as room type or corridor type according to the doors. Clustering is applied on room type and corridor type trace segments separately to produce room type and corridor type clusters. The construction of room applies the α-shape algorithm on room type clusters and the construction of corridor employs the principal component analysis(PCA) algorithm or the α-shape algorithm on corridor type clusters. Evaluations in two representative scenarios show that the method can construct the floor plan of acceptable accuracy with a relatively small set of inertial traces.
文摘In today's world, various approaches and parameters exist for designing a plan and determining its spatial, placement. Hence, various modes for identifying crucial locations can be explored when an architectural plan is designed in different dimensions. While designing all these modes takes considerable time, there are numerous potential applications for artificial intelligence (AI) in this domain. This study aims to compute and use an adjacency matrix to generate architectural residential plans. Additionally, it develops a plan generation algorithm in Rhinoceros software, utilizing the Grasshopper plugin to create a dataset of architectural plans. In the following step, the data was entered into a neural network to identify the architectural plan's type, furniture, icons, and use of spaces, which was achieved using YOLOv4, EfficientDet, YOLOv5, DetectoRS, and RetinaNet. The algorithm's execution, testing, and training were conducted using Darknet and PyTorch. The research dataset comprises 12,000 plans, with 70% employed in the training phase and 30% in the testing phase. The network was appropriately trained practically and precisely in relation to an average precision (AP) resulting of 91.50%. After detecting the types of space use, the main research algorithm has been designed and coded, which includes determining the adjacency matrix of architectural plan spaces in seven stages. All research processes were conducted in Python, including dataset preparation, network object detection, and adjacency matrix algorithm design. Finally, the adjacency matrix is given to the input of the proposed plan generator network, which consequently, based on the resulting adjacency, obtains different placement modes for spaces and furniture.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20515,62172416,52175493,U2003109,61972459,and 62102414the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2022131).
文摘The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic materials can be significantly streamlined. In this paper, we present a novel learning framework for automatically parsing floor plan images. Our key insight is that the room type text is very common and crucial in floor plan images as it identifies the important semantic information of the corresponding room. However, this clue is rarely considered in previous learning-based methods. In contrast, we propose the Row and Column network (RC-Net) for recognizing floor plan elements by integrating the text feature. Specifically, we add the text feature branch in the network to extract text features corresponding to the room type for the guidance of room type predictions. More importantly, we formulate the Row and Column constraint module (RC constraint module) to share and constrain features across the entire row and column of the feature maps to ensure that only one type is predicted in each room as much as possible, making the segmentation boundaries between different rooms more regular and cleaner. Extensive experiments on three benchmark datasets validate that our framework substantially outperforms other state-of-the-art approaches in terms of the metrics of FWIoU, mACC and mIoU.
文摘In most apptications, such as urbanism and architecture, randomty utitizing given spaces is certainty not favorable. This study proposes an expticit algorithm for utitizing the given spaces inside a rectangte with satisfactory resutts. In the titerature, connectivity is not considered as a criterion for floor plan design, but it is deemed essentiat in architecture. For example, dinin8 rooms are preferabty connected to kitchens, toitets shoutd be connected to many rooms, and each bedroom should be separated from the other rooms. This paper describes adjacency among spaces and proves that the obtained rectangular floor plan is one of the best ones in terms of connectivity. An architectural and mathematicat object catted extra spaces is introduced by the proposed algorithm and is subseauenttv examined in this work.
文摘Regulatory detailed planning is the most direct management tool for urban development and construction activities. Through theoretical deduction, this paper demonstrated the elastic relationship between the land-transferring price and the floor area ratio(FAR) of residential land, which represented the space control intensity for regulatory detailed planning. Based on the two dimensions of market regulation and fiscal replenishment, this paper also established an analytical framework for the space control intensity for regulatory detailed planning. The results showed that the space control intensity for regulatory detailed planning decreased with the expansion of urban scale and that the weaker the market regulation and the stronger the land finance dependence, the weaker the space control intensity for regulatory detailed planning. Based on this, this paper proposed that local governments should strengthen the scientificity and openness of regulatory detailed planning, institutionally resolve the predicament due to fiscal decentralization, and reduce the dependence on land transfer income.
文摘制梁场的布置形式对桥梁的施工进度和工程成本均有影响,以汉巴南高速铁路的桥梁建设工程为依托,结合SLP方法(systematic layout planning,系统化布置方法)对其中的马鞍制梁场项目进行定性和定量分析,得出合理的制梁场布局初步设计方案。再利用BIM(building information modeling,建筑信息模型)技术,建立施工现场平面布置的三维可视化模型,通过评价与决策最终确定平面布置方案。通过SLP方法与BIM技术,提高了施工现场平面布置设计和三维可视化管理的效率,避免构件在施工现场的二次搬运和交叉碰撞,可有效降低工程成本并减少安全隐患。
