POC: Clinton Stipek - [email protected]
This project's goal is to ingest morphology features (2D) and infer height (3D) for individual buildings:
- The following breaks the vivald process into the respective steps
- Identify 2D buildings from AOI
- Generate morphology features using the Gauntlet feature morphology process
- Please see Taylor Hauser ([email protected]) for availability of Gauntlet features
- Run a recursive feature eliminator to streamline modelling process
- Hyper-tune parameters via bayesian optimization
- Infer building heights at a building-by-building level leveraging a XGBoost algorithm
- There is a docker image for this project, to use the image please clone the repo and then go to vivaldi_bh/src for the docker files
- once cloned and in the right file trajectory, run the following lines in order in cmd line:
- docker-compose build vivaldi_bh
- docker-compose up -d vivaldi_by
- docker-compose exec vivaldi_bh python /files/vivaldi.py
- please note that for command 3, the 'vivaldi.py' is the vivaldi process outlined in Getting Started
- Please message Clinton Stipek ([email protected]) for assistance
- Run rfe.py (docker-compose exec vivaldi_by python /files/rfe.py - if using linux)
- Run vivaldi_bh.py (docker-compose exec vivaldi_bh python /files/vivaldi_bh.py - if using linux)
- The data that vivaldi works with is built off the Gauntlet process
- Gauntlet v2 generates 65 morphological features that is in a tabular form at a building-by-building level
- The Gauntlet features are stored in PostGresQL
- Please see Taylor Hauser ([email protected]) for access to the data
- Please see Clinton Stipek ([email protected]) for gauntlet features necessary to run rfe and vivaldi_bh