Skip to content

Project to investigate and develop spatial data-driven Geo-AI models (Convolutional Neural Network) to identify urban greenspace from Satellite images by integrating multiple data sources (e.g. vector data of urban parks)

Notifications You must be signed in to change notification settings

Spatial-Data-Science-and-GEO-AI-Lab/Park-NET-identifying-Urban-parks-using-multi-source-spatial-data-and-Geo-AI

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 

Repository files navigation

Park-NET: identifying Urban parks using multi source spatial data and Geo-AI

This is a master thesis project done by Marta Kozłowska and Jiawei Zhao for ‘Applied Data Science programme at Utrecht University. We have a goal of analysing to what extent can a reproducible CNN model that predicts urban greenspace based on open source be created. The process involved creating two CNN models. One was the U-Net model built from scratch, and the other was the U-Net with ResNet34/50 encoder to make use of the transfer learning approach.

Go to our separate folder to see which methods exactly we used and what results we got.

About

Project to investigate and develop spatial data-driven Geo-AI models (Convolutional Neural Network) to identify urban greenspace from Satellite images by integrating multiple data sources (e.g. vector data of urban parks)

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.3%
  • Python 0.7%