Funding
Funding not disclosed
Founders
Product
Problem
Planners and designers lack quantitative insights into how people actually use and move through natural and urban outdoor spaces, making it difficult to create environments that promote health, sustainability, and user satisfaction.
Solution
Landscape Computing Lab leverages big data analytics and machine learning to model human behavior in outdoor settings. By processing large-scale movement and interaction datasets, the lab identifies patterns and usage hotspots that inform evidence‑based landscape planning. The resulting computational tools enable planners to simulate design scenarios, assess potential impacts on user well‑being, and optimize layouts for ecological and social outcomes. This data-driven approach bridges the gap between qualitative observations and actionable design decisions, supporting the creation of healthier, more sustainable public spaces.
Target Audience
Primary users are landscape architects, urban planners, and environmental designers who need data-driven insights to guide the planning and design of public outdoor spaces.
Features
- Machine‑learning pipelines that ingest and analyze geospatial movement data from sensors, mobile devices, and public datasets
- Pattern‑recognition algorithms that reveal usage frequencies, flow corridors, and activity clusters in both natural and urban environments
- Interactive simulation tools allowing planners to test design alternatives and predict behavioral responses
- Visualization dashboards that map human activity heatmaps, temporal trends, and demographic breakdowns
- Exportable data models and APIs for integration with GIS software and landscape design platforms