Intelligent Architectural Briefs with Random Forest and OpenStreetMaps

Intelligent Architectural Briefs with Random Forest and OpenStreetMaps

In this post we explore how to apply Machine Learning (ML) to augment the briefing process. Presently, anyone is able to access troves of free data describing building type, tenants, property values, and energy use by location.

Intelligent Architectural Briefing

Every building design is an architect’s response to the client’s brief’ — a document containing all requirements for a project. It’s job is to explain all design constraints and goals, and covers topics such as floor areas, potential tenants, and building typology [1]. To write a good brief, a client must consider a variety of factors including urban zoning, cash-flow projections, land-values, as well as tacit business knowledge. Even before a single line is drawn, a good brief can influence the financial viability, sustainability, and even the quality of a building design.

In this post we explore how to apply Machine Learning (ML) to augment the briefing process. Presently, anyone is able to access troves of free data describing building type, tenants, property values, and energy use by location. With ML it is possible to identify hidden relationships between these datasets to predict the requirements of a new building at a specific location.

In the next section we will explain how to do this. We show how to set up a project in Python to learn from a free data source to make location-based predictions for a hypothetical development in Zurich.

We will show how to set up a project in Python to learn from a free data source to make location-based predictions for a hypothetical development in Zurich.

Getting started

For the sake of simplicity, the following example focuses on predicting the building type based on attributes extracted from the contextual data. To do the tutorial, you will need to sign up for Jupyter Notebook, a web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

To help you along, we have shared all code for this project in a GitHub repository [here_](https://github.com/rutvik-deshpande/Intelligent-Architectural-Briefs/blob/main/Intelligent-Architectural-Briefs/notebook/Predicting-BuildingProgram.ipynb)._

machine-learning artificial-intelligence real-estate architecture

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