AI is revolutionizing urban apartment design. An architect named Stanislas Chaillou developed a generative adversarial neural network (GAN) model called ArchiGAN, which can automatically generate apartment floor plans, space planning and furniture placement. 1 ArchiGAN learns the mapping from building plans to final designs through Pix2Pix technology, and can quickly generate customized apartment design plans. 2 This not only greatly improves design efficiency, but also allows users without design experience to create ideal living spaces. Although ArchiGAN currently has some limitations2 , it indicates that AI technology will have a huge impact on the field of architectural design.
As more and more well-known designers begin to use artificial intelligence technology to assist architectural design , 3 the potential of AI to transform urban interfaces has become increasingly prominent. AI technology can automate and accelerate tedious design tasks, while also providing the possibility to create new art forms. 3 In urban space, AI design can even create dreamy architectural concepts similar to the skeleton bus in “Harry Potter” 3 , injecting unprecedented creative vitality into architecture.
Key takeaways
- AI technology is subverting traditional architectural design methods
- ArchiGAN automatically generates apartment floor plans, space planning and furniture placement
- AI design can quickly produce customized apartment design plans
- AI design helps improve design efficiency and expand innovation space
- Although ArchiGAN still has limitations, it indicates that AI will have a huge impact on architectural design.
Introducing Architect GAN AI Technology
Today, as artificial intelligence (AI) technology continues to evolve, exciting breakthroughs have begun to appear in the field of architectural design. 4 Generative adversarial neural networks (GAN) were used to design floor plans and entire buildings as an artificial intelligence technology to empower architects. 4 This technology was developed at Harvard University by Stanislas Chaillou, a scholar with an architectural design background, and is called ArchiGAN.
ArchiGAN uses Pix2Pix technology to learn spatial organization and topological features from building plans, and can automatically generate the floor layout, space planning and furniture placement of the apartment. 4 This process is divided into three steps: building footprint massing, distribution procedures, and furniture layout. 4 The Pix2Pix-GAN model was used to perform a task at each step, creating a complete “generation stack” of the apartment building. 4
It is worth mentioning that ArchiGAN uses TensorFlow rather than the original version based on Torch, which proves that the technology is easy to deploy. 4 In addition, the team used NVIDIA Tesla V100 GPUs for rapid iteration and testing, training the model in less than 2 hours, saving a lot of time compared to running training locally. 4 After 250 iterations, the model developed some form of intuition. 4 These innovations have opened up new possibilities for the application of GAN technology in the field of architectural design.
ArchiGAN’s research and development work has been inspired and supported by the research results of pioneers such as Nathan Peters and Zheng Hehuang. 4The model stack consists of three models: floor space, distribution program and furniture layout. 4 Thanks to this modular design, users are able to modify or fine-tune the model’s output between each step, enabling better human-computer interaction. 4
The entire design process is very simple for the user: just enter a few constraints and boundary conditions, and the system generates the corresponding apartment design plan. 4 By connecting models one, two and three, the application of GAN technology can be extended to the entire apartment building design. 4 This flexibility allows the model to perform well in adapting to changing constraints. 4
However, ArchiGAN still needs further improvement in some details, such as solving the problem of changing floor plate shapes, improving the clarity of the output, and converting raster images into vector formats for better integration with common tools. 4 Nonetheless, it has demonstrated the huge application prospects of GAN technology in the field of architectural design, bringing new breakthroughs in efficiency and creativity to engineers and designers.
“ArchiGAN has demonstrated the huge application prospects of GAN technology in the field of architectural design, bringing new breakthroughs in efficiency and creativity to engineers and designers.”
Five core functions of ArchiGAN
ArchiGAN is a revolutionary architectural AI design tool that has five core functions, bringing unprecedented convenience and creative space to architectural designers. 5
Three-dimensional planning and comprehensive layout
ArchiGAN can not only design single-story apartments, but also realize three-dimensional architectural design of entire apartment buildings. It can automatically generate the layout of each floor to ensure that doors and windows are properly positioned, thereby creating a better architectural design. 6
DIY your house layout as you like
Users can adjust the layout of the apartment through simple painting operations and freely decide the location of doors and windows, room size and functional partitions. ArchiGAN will automatically adjust the spatial layout based on user input to meet personalized needs. In addition, ArchiGAN also has functions such as automatically configuring furniture and adapting to various irregular building spaces, which greatly improves the freedom and efficiency of apartment design. 6

AI interior design three steps to generate apartment
Using advanced AI technology can greatly improve the efficiency and creativity of interior design. 7The 15 best AI house design generators emerging in the industry, released on December 12, 2023. 7 These AI house design generators use advanced algorithms to automatically generate 3D models of homes that meet user needs, including furniture arrangements, color schemes and decorative elements. 7
ArchiGAN, a leading AI technology company, has developed an innovative apartment design process that can easily complete the three major steps of interior design. 8 First, the system will automatically generate a typical building footprint based on the input plot shape. 8 Next, AI will automatically re-divide the space and open windows based on this floor area. 8Finally , the system will automatically place corresponding furniture according to the function of each room. 8
The entire design process is automatically completed by AI. The architect only needs to enter some design constraints, and ArchiGAN can quickly generate an apartment floor plan that meets the needs. 8 This not only greatly improves design efficiency, but also enables ordinary users without a design background to independently create their ideal living space. 8 Compared with traditional manual design, this AI-automated interior design method can significantly reduce costs and time. 7
According to statistics, among the popular AI house design generators, MyRoomDesigner.ai has 1.9k monthly visits, an average stay time of 01:49 minutes, and a bounce rate of 67%. 9 Another company, Virtual Staging AI, has higher monthly visits, reaching 5.7k, with an average stay time of 06:43 minutes and a bounce rate of 40%. 9 These data reflect users’ strong demand and high recognition of AI interior design tools. 9
With the continuous advancement of AI technology and users’ pursuit of personalized design, AI interior design tools will surely play a greater role in the future. 8 This human-machine collaboration design model can not only improve efficiency, but also stimulate creativity and create a more comfortable and beautiful living environment for residents. 8
Generation from single-family to entire apartment buildings
AI design technology is not limited to single-family homes, but can also be applied to the design of entire apartment buildings. 1 ArchiGAN This AI system has five core functions, including three-dimensional planning, house DIY, etc. 1The researchers proposed an algorithm that can connect models I, II and III in sequence to process multiple apartment units at once. 2 Through this holistic approach to apartment building design, the quality and functionality of each unit can be ensured, greatly improving the efficiency and flexibility of the design.
In the past, the design of apartment buildings required treating each unit one by one, which was inefficient. 2 But ArchiGAN can plan the entire apartment at once and optimize the floor layout, zoning and furniture placement of each unit. 2 For example, ensure that each apartment unit has good lighting and ventilation by controlling the unit entrance and window position. 1
This AI-assisted apartment building design not only greatly improves design efficiency, but also ensures the quality of the overall building. 2 The ArchiGAN team has won multiple architectural awards, reflecting their professional strength in this field. 2 In the future, we can look forward to more innovative applications of AI technology in apartment building design , single-family apartment to entire building generation , and multi-family apartment intelligent design .

Application prospects: from peculiar spaces to entire communities
ArchiGAN’s application of AI technology shows great potential in the field of architectural design. In addition to being able to design standard apartments, ArchiGAN’s flexibility also allows it to handle a variety of irregular building spaces. 10 Researchers demonstrated the excellent performance of ArchiGAN in designing apartments with special shapes such as triangles, sectors, circles, etc.
In addition, ArchiGAN can also be used in the planning and design of entire building groups or communities , and can generate complex building layouts such as rings and stacks. 11 These achievements prove the broad application prospects of GAN technology in the field of urban design . In the future, it is expected to realize intelligent design from a single apartment to an entire smart community .
“ArchiGAN’s flexibility and creativity give us the opportunity to design more diversified and personalized urban living spaces.” 10
With the continuous advancement of AI technology, AI-based community planning will surely become an important part of the development of smart cities . archiGAN opens up new possibilities in this field and provides architects with innovative design tools that are expected to change people’s living experience in the future.

From single apartment to master plan
ArchiGAN can not only generate single apartment units, but also generate layout plans for entire communities or building complexes based on complex planning requirements. 11 This kind of design capability from a single entity to an overall design makes ArchiGAN have great application prospects in the field of community planning .
In the future, AI technology will provide urban designers and planners with unprecedented creative tools, allowing them to plan and design more livable community environments more intelligently. 12 This kind of AI-based community planning scheme will surely become an important part of smart city construction. 10 11 12
Limitations and future development directions
Although ArchiGAN has achieved impressive results in apartment design, it currently faces some significant limitations and challenges. 13 The first is that when designing multi-storey apartments, the design of load-bearing walls between each floor cannot fully guarantee rationality and safety. 13 Secondly, the clarity and detail of the apartment design images generated by ArchiGAN are limited, and the image resolution and visual effects need to be further improved. 13 Finally, the design plan generated by ArchiGAN cannot be directly used in actual construction and needs to be converted into a vector format for engineering construction. 13
The researchers said that future work will focus on solving these limitations, improving the practicality and reliability of ArchiGAN , and extending its application scope to more complex architectural design scenarios. 13 At the same time, AI is also facing more challenges in the field of architectural design, including the complexity of data collection and processing, and how to achieve more personalized designs. 14 Therefore, the industry needs to promote technological integration and innovation, while strengthening the training of relevant talents and educational transformation to fully realize the potential of AI in smart apartment design. 14
Looking to the future, with the continuous advancement of AI technology, ArchiGAN and other related AI architectural design tools will play an increasingly important role in providing users with a smarter and more comfortable living experience and improving design efficiency. 14 At the same time, emerging technologies such as blockchain also have broad application prospects in the field of apartment design, bringing new opportunities for future smart apartment design. 15

In general, although ArchiGAN and other AI architectural design tools still have some limitations, through continuous research and development and innovation, they will become an indispensable and important tool for future urban apartment design, injecting new ideas into the development of smart apartments. power. 13 14 15
Observe the city through AI eyes
In recent years, with the continuous advancement of artificial intelligence (AI) technology, a new way of urban observation is emerging – using 16 computer vision technology to identify and analyze various visual features of urban space from a pedestrian perspective. This urban observation method based on 16 visual analysis is injecting new vitality into urban design research and providing planners and designers with more accurate and quantitative urban space evaluation methods.
In the past 16 20 years, computer vision technology has been increasingly used in the field of urban design research. From the initial sentiment analysis and street scene evaluation to today’s pedestrian behavior analysis and landscape modeling, these analysis methods based on 16 visual data are being used in conjunction with The subjective evaluations are integrated with each other to provide urban planners with more comprehensive and objective decision-making support.
In 16 China, different types of urban visual data, including data collected through public participation, corporate proprietary data, and manually collected data, are being widely used in urban design practice. At the same time16 , collaborations between designers and computer vision companies have become more frequent, exploring how to best use visual analysis technology to measure and improve the quality of urban space.
These new methods based on 16 AI and visual analysis have brought transformative impacts to areas such as urban morphology analysis, public participation assessment, and street quality evaluation. For example, by identifying and classifying point cloud data using 16 semantic segmentation technology, designers can more accurately assess the environmental impact of urban landscapes; and methods based on 16 pedestrian behavior analysis can help understand the interaction patterns between people and space.
Although 16 AI-driven urban observation methods have made many breakthroughs, they also face some challenges, such as potential data bias issues, which require the participation and verification of designers and the public. 16 Only through multi-faceted efforts can visual-based urban space analysis truly become an indispensable and important tool in urban design practice.

Application of computer vision technology in urban design | Content description | references |
---|---|---|
Street quality assessment | Based on image segmentation and target detection technology, indicators such as green visibility and permeability of streets can be measured. | 16 |
Public participation effect analysis | By analyzing visual data from a pedestrian perspective, the emotional attributes and user experience of urban spaces can be assessed | 16 |
behavioral pattern recognition | Using technologies such as video analysis, people flow dynamics and activity patterns in public spaces can be analyzed | 16 |
Landscape and environment modeling | Through the classification and semantic segmentation of point cloud data, the accuracy of urban landscape assessment can be improved. | 16 |
“Urban observation in the AI era requires the joint participation of designers and the public. Only by making full use of visual data and combining it with subjective experience can we truly understand the nature of urban space.”
—A well-known urban planning expert
Measuring street space quality
Through AI-based urban observation methods, we can effectively evaluate different characteristics of street spatial quality. For example, computer vision analysis of large-scale street view images can be used to assess street permeability , i.e., the distribution of opening features such as doors and windows across the entire street facade. 17 At the same time, analyzing the color characteristics of street view images can also quickly evaluate the greenness ratio of the street , that is, the degree of vegetation coverage. 17 These AI-based street space analysis methods provide objective and quantitative evaluation indicators for urban design, helping to optimize the urban pedestrian environment from a “humanistic” perspective.
Street Permeability Assessment
Street permeability refers to the opening characteristics of buildings along the street, such as the distribution of elements such as doors and windows across the entire street facade. This indicator reflects the degree of interaction between streets and buildings, as well as people’s experience of activities on the streets. Through computer vision analysis of large-scale street view images, we can quickly assess the permeability of streets, providing an important reference for urban design. 17
Street green visibility analysis
Street green visibility refers to the proportion of vegetation coverage along the street, which reflects the natural elements in urban public spaces. High green visual acuity means a more comfortable and pleasant walking environment, which helps promote physical and mental health. By using AI technology to analyze the color characteristics of street view images, we can quickly evaluate the green visibility of streets, providing an objective basis for urban planning and design. 17
index | definition | Analytical method | significance |
---|---|---|---|
street penetration | Distribution of building opening features along streets | Computer Vision Analysis of Street View Images | Reflect the interaction between streets and buildings, affecting the walking experience |
street green visibility | Proportion of vegetation coverage along streets | Analyze color characteristics of Street View images | Reflect natural elements in urban public spaces and are related to health |
In summary, the street space analysis method based on AI technology provides objective and quantitative evaluation indicators for urban design, which helps to optimize the urban pedestrian environment from a “humanistic” perspective. 17 These indicators not only reflect the physical characteristics of the street itself, but are also closely related to people’s physical and mental health and activity experience, providing an important basis for urban planning and design.

Analyze interaction patterns between people and space
In the field of urban design, understanding people’s behavioral patterns in public spaces is a critical task. 18 By conducting computer vision analysis on videos recorded in street spaces, researchers can restore the activity trajectories and behavioral characteristics of pedestrians in public spaces. This kind of human-space interaction research based on video analysis provides objective and detailed data support for urban design and helps optimize the function and quality of public space.
Specifically, researchers can take the following steps: First, mark the position of each pedestrian in each frame in the video to form a pedestrian movement trajectory map. 18 Secondly, with the help of target detection algorithms such as YOLOv5, pedestrians’ trajectory, speed, direction and other behavioral characteristics can be more accurately identified, and spatio-temporal heat maps can be drawn. 18 This kind of behavioral pattern recognition based on video analysis provides objective and detailed human-space interaction data for urban design, helping to optimize the function and quality of public spaces.

In addition to visual analysis, this video-based behavioral pattern recognition technology can also be combined with other data sources, such as geographical location data, user characteristics, etc., to further explore people’s activity habits and preferences in public spaces. 19 In the future, with the continuous advancement of computer vision technology, this human-space interaction analysis method will surely play a more important role in urban design, planning and management.
“Traditional observation and survey methods have limitations and cannot fully restore people’s behavioral characteristics in urban space. Video-based computer vision analysis provides us with a new research idea that can more accurately capture people and space ” interactive mode.”
In the field of urban design, the analysis of human-space interaction has become an indispensable tool. 19 By combining big data and artificial intelligence technologies, we can better understand the needs of urban residents, optimize the experience of using public spaces, and improve the livability of cities.
Data sources and visual analysis tools
The acquisition channels of urban visual data are mainly divided into three categories: 20 1) Crowdsourcing (social media) data; 2) Private (street view map) data; 3) Manual collection of data. Among them, street view image data and manually collected data are widely used in research. The former has time series continuity, while the latter can obtain richer metadata information.
In addition to professional computer vision algorithms, there are also some relatively simple visual analysis tools, such as Clarifai, CloudCV and other APIs, as well as open source platforms such as Makesense and YOLO, which can be used by practitioners engaged in urban design. 21 In addition, there are also some new urban science research companies providing AI-based urban visual analysis services.
Urban visual data source | Features |
---|---|
Crowdsourced (social media) data | Data sources are extensive and time series are continuous |
Private (Street View Maps) Data | High data quality, richer metadata information can be obtained |
Manual data collection | Data control is more precise and can meet personalized needs |
22 Data visualization is the process of representing data using visual elements such as charts, graphs, or maps. Modern businesses deal with vast amounts of data from a variety of sources, including internal and external websites, smart devices, internal data collection systems and social media. Data visualization improves customer service, increases employee engagement, and drives strategic decision-making.

All in all , 20 computer vision tools and 21 AI-based urban analysis platforms provide urban design professionals with a variety of options, which can effectively obtain and analyze various types of 22 urban visual data, providing important support for urban planning and construction.
in conclusion
With the continuous advancement of artificial intelligence technology, urban observation methods based on computer vision are bringing new opportunities to the field of urban design. 23 By analyzing large-scale street view images and video data, various quality indicators of urban space, such as street penetration rate, green vision rate, etc., can be objectively and precisely evaluated. 24 At the same time, behavioral pattern recognition from the perspective of pedestrians also provides valuable data support for optimizing public space functions. 23 This urban observation method that integrates computer vision technology will surely become an indispensable and important tool in future urban design practice.
24 The application of AI in urban design is showing broad prospects. From spatial analysis and planning optimization to automatic 3D model generation and material color matching recommendations, all aspects can be automated and intelligent with the help of AI technology, greatly improving design efficiency and solution quality. 23 Thisadvanced design method based on the application of AI in urban design is expected to be fully integrated with the future of computer vision technology in urban planning , contributing to creating a more livable urban environment.
However , AI technology still faces some challenges in interior design. It is difficult to completely replace the creative thinking of designers. There are also certain limitations in personalized needs and complex design scenarios . Vision-based urban observation methods also need to solve technical problems in specific applications and be better integrated with humanistic design concepts in order to truly exert their effectiveness in urban renewal practice. In the future, we have reason to believe that the application prospects of artificial intelligence in the field of urban design will be even broader.
FAQ
How is artificial intelligence (AI) bringing revolutionary changes to urban apartment design?
Architect Stanislas Chaillou developed a generative adversarial neural network (GAN) model called ArchiGAN that can automatically generate apartment floor plans, space planning and furniture placement. ArchiGAN learns the mapping from building plans to final designs through Pix2Pix technology, and can quickly generate customized apartment design plans. This not only greatly improves design efficiency, but also allows users without design experience to create ideal living spaces.
What are the five core functions of ArchiGAN?
ArchiGAN’s five core functions include: 1) three-dimensional planning and comprehensive layout, which can design the three-dimensional architecture of the entire apartment building; 2) DIY apartment layout, users can freely adjust the layout of the apartment; 3) automatically configure furniture; 4) adapt to various An irregular architectural space; 5) greatly improves the freedom and efficiency of apartment design.
How does the ArchiGAN apartment design process work?
ArchiGAN’s apartment design is divided into three steps: 1) Generating a volume model of the building’s footprint; 2) Automatically re-dividing the space and opening windows; 3) Automatically placing corresponding furniture according to the function of each room. The entire design process is automated. The architect only needs to enter some design constraints, and ArchiGAN can quickly generate an apartment floor plan that meets the needs.
ArchiGAN can not only design single-family apartments, but can it also be extended to the design of entire apartment buildings?
Yes, ArchiGAN can connect Models I, II, and III in sequence to process multiple apartment units at once. This not only meets the design needs of the entire apartment, but also ensures the quality and functionality of each unit, such as controlling entrances and window positions to ensure good lighting and ventilation. This holistic approach to apartment building design greatly improves design efficiency and flexibility.
What are the current limitations of ArchiGAN?
ArchiGAN currently has some limitations: 1) When designing multi-story apartments, the design of load-bearing walls between each floor cannot be completely rational; 2) The clarity of the output image is limited, and the resolution and detail expression need to be improved; 3) The generated design scheme cannot be directly used in actual construction and needs to be converted into vector format. The researchers said that future work will focus on solving these limitations and improving the practicality and reliability of ArchiGAN.
What is the important role of AI-based city observation methods?
AI-based urban observation methods can effectively evaluate different characteristics of street spatial quality, such as evaluating street permeability and green visibility. At the same time, analyzing street view images and video data can also restore pedestrian activity patterns in public spaces, providing objective and detailed human-space interaction data for urban design. This urban observation method that integrates computer vision technology will surely become an indispensable and important tool in future urban design practice.
What are the main channels for obtaining urban visual data?
The acquisition channels of urban visual data are mainly divided into three categories: 1) crowdsourcing (social media) data; 2) private (street view map) data; 3) manual collection of data. Among them, street view image data and manually collected data are widely used in research. The former has time series continuity, while the latter can obtain richer metadata information. In addition, there are also some relatively simple visual analysis tools and AI analysis service platforms that can be used by practitioners engaged in urban design.