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Table of Contents
Project developed under the course AI in Architecture 2023-24 in IAAC.
Description: SkinSense predicts the indoor thermal comfort accordingly to the combination of the window ratio on the facade and the shading system. SkinSense will use Pix2Pix model that will be trained on a data set of a combination of facade encoded RGB images and a honeybee indoor floor heat maps. why, what, how (where and for whom)
Problem statement: which problem the project solves, why you are doing it.
Idea: what you are doing to solve the problem
Solution: how you are solving the problem, the method
Place: Cairo
Beneficiaries: the users of the project, who will benefit from it.
None
To use the project follow these steps:
The user needs to input a Geometry for the extrior walls and the openings of a building floor
While working on the project the following challenges were encountered:
Describe which challenges you faced during the project (e.g. there's an issue with missing public facilities data in Beirut, instability of image generation from prompts etc.)
- challenge 1
- challenge 2
Describe the potential improvements or developments of the project (e.g. deploy the project, add more cities, add support for some feature).
- step 1
- step 2
- step 3
Distributed under the MIT License. See LICENSE.txt for more information.
AUTHORNAME - @AUTHORNAME - e-mail - LinkedIn
* [Best README template](https://github.com/othneildrew/Best-README-Template)