The AGILE Landscape Project seeks to improve cities and public space through ADAPTIVE GENERATIVE and INTELLIGENT LANDSCAPE EXPERIMENTATION. The project draws inspiration from both the agile software model and the Lean Startup methodology. The agile software development model is based on iterative incremental development process for solving problems through self-organizing and cross-functional teams. While similar, the Lean Startup methodology creates an environment of experimentation where minimal viable products (MVPs) are developed to quickly validate solutions. These approaches have also inspired tactical urbanism projects throughout the world where citizens are incrementally experimenting with the creation of new public spaces. The AGILE Landscape Project seeks to take this iterative urbanism one step further to create permanent landscape solutions that are adaptable to the needs of the user. The intelligence aspect of the project explores the use of sensors and big data to better manage the landscape and to develop environments responsive to the user. When feasible, the AGILE Landscape Project focuses on building physical experiments and deploying them within the real word versus relying on computer generated renderings and models to demonstrate ideas.

The project is led by Brian Phelps. Mr. Phelps is a senior associate at Hawkins Partners, Inc., a landscape architecture and urban design firm in Nashville, TN where he has worked since 1998. As a landscape architect, Mr. Phelps explores viable market-based solutions for repairing our cities and improving our urban experiences. He is an avid maker and supporter of the maker movement.

Don Tolley serves as an advisor for the AGILE Landscape Project.  He has over twenty-fiver years of experience software and hardware development.  He is based out of Alexandra, VA where he currently works in the digital interactive market space.  He specializes in cutting edge computer vision and robotics technologies.   Additionally he enjoys working with artificial intelligence algorithms.  

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