In the face of climate change and urbanization, Nature-Based Solutions (NBS) have become essential tools for sustainable urban development. Landscape architects play a key role in integrating green infrastructure, biodiversity, and ecological resilience into city planning. However, designing these solutions requires balancing ecology, aesthetics, functionality, and sustainability—a challenge where Artificial Intelligence (AI) can be a game-changer.

AI is more than just automation; it can enhance creativity, optimize planning, and improve implementation. This guide outlines step-by-step methods for using AI to design, simulate, and implement NBS effectively.

To achieve this, we are leveraging technologies such as Dutch Cycling Lifestyle, Stable Diffusion, Segment Anything, and LLMs (Large Language Models) to generate prompts that suggest potential vegetation suited to specific urban environments. The tool relies mainly on Stable Diffusion, which generates images using text prompts on a model trained on extensive image datasets.

This model also enables "inpainting", allowing users to modify specific areas of an existing picture. To determine these areas, we use a segmentation model that detects roads, vehicles, and other unwanted objects in the original images.

All of this is built within ComfyUI, a visual node-based interface for AI image processing, making it easy to modify workflows and add new features for landscape design.

Additionally, we are integrating our research with NBSeduWorld, a platform dedicated to promoting education and innovation in Nature-Based Solutions. This collaboration enhances our AI-driven landscape tools by ensuring they align with the latest knowledge and best practices in urban biodiversity and green infrastructure.

Step-by-Step Guide to AI-Driven NBS Implementation

Step 1: Find a Potential NBS Implementation

🔹 Identify an area where a Nature-Based Solution (Learn) can be applied, such as: ✅ An urban heat island that needs cooling. ✅ A flood-prone area that could benefit from wetland restoration. ✅ A car-dominated street that could be transformed into a green corridor.

Step 2: Research NBS Approaches

🔹 Use NBSeduWorld to explore case studies and educational resources on proven NBS implementations. ✅ Learn about different NBS strategies that match your target area. ✅ Understand the ecological and socio-economic benefits of each solution.

Urban Nature Atlas – A database of NBS projects worldwide.
Envision Tomorrow – A planning tool for sustainable urban design.
Nature-Based Solutions Toolbox – A tool for assessing NBS feasibility.
The Economics of Ecosystems and Biodiversity (TEEB) – Tools for valuing ecosystem services.
Oppla – A knowledge-sharing platform for NBS professionals.

Step 3: Segment the Picture and Remove Unwanted Elements

🔹 Upload an image of the selected area. 🔹 Use Segment Anything to identify and remove unwanted elements like: ✅ Excessive road surfaces. ✅ Unused parking lots. ✅ Non-permeable infrastructure that can be replaced with green areas.

Step 4: Prompt AI for Future Climate-Resilient NBS

🔹 Use Stable Diffusion and LLMs to generate AI-assisted design proposals by: ✅ Crafting prompts that include specific plant species adapted to the region. ✅ Including climate adaptation considerations, such as flood mitigation or drought-resistant plants. ✅ Generating multiple variations to explore different landscape possibilities.

“*”Generate a detailed visualization of the urban vegetation in Marseille, France, under a future climate scenario (year 2050-2100). Use predictive climate data (IPCC projections) to determine the expected temperature, precipitation changes, and drought frequency. Identify plant and tree species that will be resilient under these conditions, focusing on:

Drought-resistant Mediterranean species
Urban-adapted trees for heat mitigation
Coastal and wind-resistant vegetation
Biodiversity-supporting native flora
Consider species like Quercus ilex (Holm Oak), Pinus halepensis (Aleppo Pine), Pistacia lentiscus (Mastic Tree), Nerium oleander (Oleander), and Arbutus unedo (Strawberry Tree) that are expected to thrive.
Generate an AI-assisted landscape design integrating these species into Marseille’s urban infrastructure, including green roofs, cooling corridors, and water-efficient public gardens.”*”

— OPENAI

Step 5: Share Your Vision

🔹 Present your AI-generated concept to: ✅ Urban planners and policymakers to advocate for implementation. ✅ Community stakeholders for feedback and engagement. ✅ Design and architecture professionals for refinement and execution.

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Conclusion:

AI is transforming landscape architecture and Nature-Based Solutions by offering data-driven insights, rapid prototyping, and predictive modeling. By utilizing, Stable Diffusion, Segment Anything, and LLMs, landscape architects can achieve smarter, more sustainable, and highly customized urban green spaces pictures.

Our work is further enriched by insights from NBSeduWorld, ensuring our AI-driven approaches are aligned with the latest research and best practices in urban biodiversity.

🌿 As AI and NBS continue to evolve, urban landscapes will become more adaptive, resilient, and sustainable.

🚀 Want to implement AI-driven landscape solutions? Start by integrating AI tools into your next project!

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