INASLA_AI and Landscape Architecture.pptx
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How can we overcome our fears, address necessary skepticism, and unlock the power of the next generation technology ecosystem to enhance the profession of landscape architecture? This session will explore the forefront of innovation, discussing how AI-driven solutions will optimize planning, visualization, detailing, and resource management within the profession. These tools will play a pivotal role in how landscape architects shape a greener, smarter, and more beautiful world.
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INASLA_AI and Landscape Architecture.pptx
- 1. The Transformative Impact of Artificial Intelligence on Landscape Architecture: Enhancing Design, Planning, and Sustainability INASLA Conference on Landscape Architecture Jonathon Geels, PLA, FASLA, MBA | Troyer Group August 2023 //imagine frederick law olmsted sr taking selfie landezine.com/using-artificial-intelligence-in-your-design-process
- 2. Learning Objectives: Understand the fundamental concepts of artificial intelligence (AI) and its application in LA Explore innovative AI-driven design tools and techniques for LAs Examine the role of AI in enhancing planning and decision-making in LA Discuss AI-enabled sustainability strategies and ecological design Address the ethical and social implications of AI in LA mckinsey.com/mgi/our-research/generative-ai-and- the-future-of-work-in-america
- 3. Introduction: The role of AI in landscape architecture’s contemporary evolution and future ascension Using Artificial Intelligence in Your Design Process landezine.com Explanation of Artificial Intelligence (AI) in the Context of Landscape Architecture and its Transformative Impact on the Profession
- 6. reddit.com/r/LandscapeArchitecture/comments/12h2821 /beautiful_landscape_and_urban_plans_by_midjourney Understanding AI in Landscape Architecture: Explanations, Examples, Adoption Concerns, Key Concepts
- 7. 1. ArtificialNarrowIntelligence 2. ArtificialGeneralIntelligence 3. ArtificialSuperintelligence 4. ReactiveMachines 5. LimitedMemory 6. TheoryofMind 7. Self-aware AI TYPES builtin.com/artificial-intelligence/types-of-artificial- intelligence
- 9. Environmental Impact Assessments Ease of use, adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints “Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production”
- 11. CONCERNS + FEARS In Landscape Architecture
- 12. Screen capture from “Terminator 2: Judgement Day” Tri-Star Pictures | Released 1991 Significant Concerns with Generative AI Bias + Fairness Misinformation + Deepfakes Intellectual Property + Copyright Lack Of Control + Accountability Security + Privacy
- 13. AI Won’t Replace Humans: But Humans With AI Will Replace Humans Without AI Karim Lakhani | Harvard Business School www.hbr.com Addressing Concerns and Fears Surrounding AI In Landscape Architecture
- 14. AI Applications for Design, Planning, and Business: Collaborations, Case Studies, Key Concepts architosh.com/2022/12/autodesk-cloud-based- platforms-and-ai-ml-aim-to-help-solve-the-biggest- problems-we-face/
- 15. Generative Design + Parametric Models Iterative process streamlines design exploration and optimization, enabling discovery of the most efficient and effective solutions. Predictive Analytics + Simulation Simulate various environmental, structural, and operational scenarios to predict how a building will perform in different conditions. Material + Resource Optimization This iterative process streamlines design exploration and optimization, enabling architects to find the most efficient and effective solutions. Design Process Optimizations
- 16. CASE STUDY: Dreaming Up Viable Parametric Creations: Working Through The Chaos Of AI AI-assisted parametric design leverages the power of artificial intelligence and machine learning algorithms to create complex, data-driven models. parametric-architecture.com/dreaming-up-viable-parametric-creations- working-through-the-chaos-of-ai/
- 17. CASE STUDY: The Emergence of Geospatial Artificial Intelligence: GeoAI Transforming the speed at which we extract meaning from complex datasets. • Extract rich geospatial data with deep learning • Perform predictive analysis using machine learning • Improve data quality, consistency, and accuracy • Accelerate the time to situational awareness • Bring location intelligence to decision- making www.esri.com/en-us/capabilities/geoai/overview
- 18. CASE STUDY: AR/VR + AI In AR, AI can improve object recognition and tracking, making digital overlays seamlessly integrate with the real world. moonpreneur.com/blog/role-of-ai-in-ar-and- vr/#:~:text=AI's%20integration%20with%20AR%20and,behaviors%20and%2 0optimizing%20content%20delivery.
- 19. PRO-TIP: Prompt-craft The art of generating images with text, feels simple to understand and hard to master. discord.com
- 20. PRO-TIP: Mapillary Extract geospatial and visual imagery data, customize toolkits within existing platforms. mapillary.com
- 21. Shaping a Greener, Smarter, and More Beautiful World: AI Potential, Future Opportunities and Challenges, Key Concepts www.thevintagenews.com
- 22. www.esri.com/en-us/artificial-intelligence/overview FUTURE OPPORTUNITY: Conservation + Restoration: AI can aid in identifying areas for conservation and habitat restoration. Machine learning models can recognize invasive species, monitor wildlife movements, and assess the health of ecosystems. This information can guide efforts to restore and rehabilitate degraded landscapes.
- 23. Bishan-Ang Mo Kio Park www.asla.org/2016awards/169669.html FUTURE OPPORTUNITY: Land Use Planning: AI algorithms can assist in optimizing land use planning by considering factors such as ecological value, wildlife habitats, water resources, and human activities. This can lead to more sustainable urban development, protection of natural habitats, and the preservation of green spaces within cities.
- 24. Conclusions: Future Opportunities + Challenges Discord.com
- 25. Screen capture from “Terminator 2: Judgement Day” via Tri-Star Pictures | Released 1991
Editor's Notes
- Computers have felt smart – even if in only a semi-dumb kind of way – for a long time. But never before have they felt so creative. How can we overcome our fears, address necessary skepticism, and unlock the power of the next generation technology ecosystem to enhance the profession of landscape architecture? This session will explore the forefront of innovation, discussing how AI-driven solutions will optimize planning, visualization, detailing, and resource management within the profession. These tools will play a pivotal role in how landscape architects shape a greener, smarter, and more beautiful world.
- GOALS 1. Understand the fundamental concepts of artificial intelligence (AI) and its application in landscape architecture: This learning objective aims to provide the audience with a foundational understanding of AI and its relevance to landscape architecture. 2. Explore innovative AI-driven design tools and techniques for landscape architects: This objective focuses on introducing the audience to cutting-edge AI-driven tools and techniques that can revolutionize the design process in landscape architecture. 3. Examine the role of AI in enhancing planning and decision-making in landscape architecture: This objective delves into how AI can support landscape architects in the planning phase by analyzing large datasets, identifying patterns and trends, and generating valuable insights. 4. Discuss AI-enabled sustainability strategies and ecological design: This learning objective centers around the integration of AI technologies in promoting sustainability and ecological design principles within landscape architecture. 5. Address the ethical and social implications of AI in landscape architecture: This objective focuses on the ethical considerations and potential societal impacts of integrating AI into landscape architecture practices.
- AI-driven tools and algorithms have emerged as powerful resources for landscape architects, enabling them to analyze vast amounts of environmental data, predict climate change impacts, optimize design elements, and create sustainable and resilient landscapes. Embracing AI's capabilities can empower landscape architects to address the pressing challenges of urbanization, climate change, and biodiversity loss, paving the way for a sustainable and visionary future in landscape architecture. Deeper, faster, and more expansive analysis Predictive modeling and data-driven decision-making In 2021, Torey crafted a remarkable vision for the future of Landscape Architecture and ASLA, launching a strategic plan that integrates community, connections, innovation, scale, and voice – for that trajectory to remain relevant – the profession (no matter the scale of practice) must remain at the forefront of these conversations – we don’t have the luxury to sit idly by
- From generating complex 3D models to simulating vegetation growth and water flow patterns, AI augments the design process, fostering creativity, efficiency, and precision. While AI can help supplement our perspective and plan rendering, graphic integrations (like Photoshop Generative fills) are very surface level implementations – I’m far more interested in AI’s potential to expand design’s impact and role in future capacity building and problem solving.
- New research from Harvard Business Review Analytic Services finds that market forces, sustainability initiatives, and the need to attract younger workers all increase the pressure for businesses to innovate. 12M occupational transitions through 2030 The accelerated development of generative AI, with its advanced natural language capabilities, has extended the possibilities for automation to a much wider set of occupations. From a business standpoint, think of the major disruptions in Landscape architecture and design business over the last 50 years: CAD and other visualization tools, 3D modeling, digital collaboration, online marketing
- As AI becomes more sophisticated, it holds the potential to revolutionize landscape architecture by integrating with smart city initiatives, supporting real-time data monitoring, and enhancing user experience. Traditional AI systems are primarily used to analyze data and make predictions .. Traditional AI excels at pattern recognition Generative AI goes a step further by creating new data similar to its training data .. Generative AI excels at pattern creation
- From chatbots to super-robots, here’s the types of AI to know and where the tech's headed next. Artificial Narrow Intelligence: AI designed to complete very specific actions; unable to independently learn. Artificial General Intelligence: AI designed to learn, think and perform at similar levels to humans. Artificial Superintelligence: AI able to surpass the knowledge and capabilities of humans. Reactive Machines: AI capable of responding to external stimuli in real time; unable to build memory or store information for future. Limited Memory: AI that can store knowledge and use it to learn and train for future tasks. Theory of Mind: AI that can sense and respond to human emotions, plus perform the tasks of limited memory machines. Self-aware: AI that can recognize others’ emotions, plus has sense of self and human-level intelligence; the final stage of AI.
- ChatGPT and Midjourney didn’t pop-up over night – slowly acclimating 1938-1946 – golden age of science fiction 1942 – Bombe WWII encryption machine 1949 – Machester Mark 1 – first program computer 1950 – Can machines think? Alan Turing 1956 Dartmouth Summer Research Project on AI (John McCarthy) 1961 – Unimate – first industrial robot 1963 – DARPA funds AI at MIT 1970s – Start predicting AI surpassing human intelligence within the next decade 1986 – Navlab first autonomous car by Carnegie Melon 1997 – Deep Blue 2011 – IBM Watson SuperComputer/Apple’s SIRI 2014 - Alexa
- https://www.sciencedirect.com/science/article/abs/pii/S0048969718308428?via%3Dihub This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS) Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints.Â
- Emerging field of “Urban Visual Analytics” Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data Using Urban Mosaic to inspect the presence and condition of tactile pavements in NYC, which is important to provide safe pedestrian access to people with movement or vision impairment disabilities. (a) Query image. (b) Three examples of other images in the database similar to the query image. (c) Visualizing the density of all images in NYC similar to the query image as a heatmap. The highlighted region is set as a spatial constraint to query only for images similar to the query image in that region. (d) Three example images taken at different times showing that the pavement at that location is in dire need of repairs in May 2016, and was later repaired before August 2016 StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment.
- Addressing concerns and fears surrounding AI adoption in landscape architecture. Generative design uses AI to expedite the computer-aided design process. Generative design helps with ideation, generating all computationally possible solutions to a problem within a given set of parameters -- even when the design is completely novel and a radical change from anything that has come before Generative AI tools can help office administrators and assistants with tasks such as basic email correspondence, identifying data trends, finding mutually available meeting times across time zones and other summary/synthesis exercises. Microsoft 365 Copilot -- a collection of AI-powered tools integrated into Microsoft's productivity suite -- could radically increase office workers' productivity. As of this writing, the tool is in beta testing and is expected to be released to the public later in 2023
- Bias and Fairness: Generative AI models learn from large datasets, which can inadvertently perpetuate biases present in the data. If the training data contains stereotypes or discriminatory patterns, the generated content might also reflect those biases. This raises concerns about fairness, ethics, and potential reinforcement of societal inequalities. Misinformation and Deepfakes: Generative AI can be used to create highly realistic fake content, including text, images, and videos. This can lead to the creation and spread of misinformation, fake news, and malicious content. Deepfakes, in particular, can be used to create convincing videos of individuals saying or doing things they never actually did, which poses serious risks to public trust and security. Intellectual Property and Copyright: Generative AI can generate content that resembles existing creative works, raising questions about intellectual property and copyright infringement. Creators and artists might find their work replicated or altered without permission, potentially impacting their livelihoods and rights. Lack of Control and Accountability: As generative AI becomes more sophisticated, it can generate content that is difficult to distinguish from human-created content. This raises concerns about maintaining control over the generated output and determining accountability for content created by AI. It becomes challenging to discern whether content was created by a human or AI, which can have legal, ethical, and reputational implications. Security and Privacy: The use of generative AI for malicious purposes, such as creating phishing emails, social engineering attacks, or fake identities, can compromise security and privacy. Additionally, the technology could be used to breach privacy by generating convincing fake personal information or compromising images.
- Just as the internet has drastically lowered the cost of information transmission, AI will lower the cost of cognition. How fast can you understand the scale of something
- Collaborating with AI systems and human expertise in landscape architecture. i. Optimizing design processes with AI tools and algorithms. ii. AI's role in enhancing planning and decision-making in landscape architecture. Key concepts may include generative design, parametric modeling, virtual reality (VR) simulations, and AI-assisted site analysis, enabling more efficient and creative design solutions.
- Generative Design and Parametric Modeling: AI-powered generative design tools can rapidly explore numerous design iterations based on specified parameters and constraints. These tools create a range of options that human designers might not have considered, helping to identify innovative solutions. Parametric modeling, which is often used in conjunction with generative design, allows designers to establish relationships between design elements and adjust them dynamically. This iterative process streamlines design exploration and optimization, enabling architects to find the most efficient and effective solutions. Predictive Analytics and Simulation: AI can simulate various environmental, structural, and operational scenarios to predict how a building will perform in different conditions. This predictive capability is crucial for optimizing energy efficiency, occupant comfort, and overall sustainability. For instance, AI can analyze data related to daylighting, thermal comfort, and airflow to inform design decisions that minimize the building's environmental impact. This data-driven approach helps architects make informed choices and fine-tune designs before construction begins, reducing the likelihood of costly changes during later stages. Material and Resource Optimization: AI can assist architects in selecting the most appropriate materials and construction techniques for a project. By analyzing factors such as durability, cost, availability, and environmental impact, AI systems can suggest optimal choices that align with the project's goals and constraints. Additionally, AI can help minimize waste and maximize resource efficiency by generating optimized cutting patterns for materials, reducing excess material consumption during construction. This contributes to both sustainability and cost-effectiveness.
- https://parametric-architecture.com/dreaming-up-viable-parametric-creations-working-through-the-chaos-of-ai/ Parametric design is a design method in which features, such as building elements and engineering components, are shaped based on algorithmic processes rather than direct manipulation. In this approach, parameters and rules establish the relationship between design intent and design response.[1][2][3] The term parametric refers to the input parameters that are fed into the algorithms
- GeoAI is transforming the speed at which we extract meaning from complex datasets, thereby aiding us in addressing the earth’s most pressing challenges. It reveals and helps us perceive intricate patterns and relationships in a variety of data that continues to grow exponentially. Organizations leveraging GeoAI are revolutionizing how they turn data into information, with models that adapt even as data evolves.Â
- Reduce visual clutter AI-driven context awareness enhances the relevance of information displayed, adapting to user surroundings and needs. Natural language processing enables more intuitive interaction, allowing users to communicate with AR systems using voice commands or gestures.
- How words are strung together significantly affects the output of the AI. Grammatical thinking can be disregarded. Words, their close connection, and placement can be “felt”, more like abstract art than literature. Power User of Google
- Machine-generated map features
- Precision Agriculture: AI-powered precision agriculture techniques can enable farmers to use resources more efficiently. Sensors, drones, and satellite imagery can provide real-time data on soil moisture, nutrient levels, and crop health. AI algorithms analyze this data to optimize irrigation, fertilization, and pest control, reducing waste and minimizing environmental impact. Climate Change Mitigation: AI can contribute to climate change mitigation by analyzing data related to carbon emissions, deforestation rates, and renewable energy potential. This information can guide policies and strategies aimed at reducing greenhouse gas emissions and transitioning to cleaner energy sources. Disaster Management: AI-driven predictive models can help forecast natural disasters such as wildfires, floods, and landslides. By analyzing weather patterns, historical data, and real-time observations, these models can provide early warnings and inform evacuation plans, ultimately saving lives and minimizing damage. Monitoring and Enforcement: AI can automate the monitoring of protected areas and wildlife reserves. Using cameras, sensors, and image recognition algorithms, AI can detect illegal activities such as poaching and logging, allowing authorities to respond promptly and protect vulnerable ecosystems. Energy Efficiency and Infrastructure Planning: AI can optimize energy consumption in buildings and urban infrastructure. Smart grids and AI-controlled systems can balance energy supply and demand, reducing waste and carbon emissions. Additionally, AI can aid in designing energy-efficient buildings and transportation systems.
- Site selection, spatial analysis, urban planning, and environmental impact assessment, fostering more informed and sustainable decision-making processes.
- Site selection, spatial analysis, urban planning, and environmental impact assessment, fostering more informed and sustainable decision-making processes.
-  In the context of design, AI is a tool that requires a designer’s control and curation. Without the designer, AI is stuck as abstract art and cannot develop into the realm of utility. The speed of AI advancement is remarkable, but he strongly believes we are still in the infancy of this technology. Its current form is uncontrollable and chaotic, but surely it will progress towards achieving more control and finetuning ability. This will inevitably shake all fields of architectural design and visualization.
- AI Ethics and Regulations: As AI becomes more integrated into society, ethical considerations and regulations surrounding its use will become increasingly important. Ensuring fairness, transparency, and accountability will be key. AI and Jobs: Automation driven by AI could impact various job sectors, potentially leading to job displacement in some areas while creating new opportunities in others. Education and reskilling will be crucial to address these shifts. Computational Challenges: Developing more powerful hardware and more energy-efficient AI algorithms will be critical to sustaining AI advancements.
- Alan Turing: "We can only see a short distance ahead, but we can see plenty that needs to be done.“