Summary of Predicting Building Types and Functions at Transnational Scale, by Jonas Fill et al.
Predicting building types and functions at transnational scale
by Jonas Fill, Michael Eichelbeck, Michael Ebner
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates whether it is feasible to predict building types and functional classes at a European scale using open GIS datasets available across countries. A graph neural network (GNN) classifier was trained on a large-scale graph dataset consisting of OpenStreetMap (OSM) buildings across the EU, Norway, Switzerland, and the UK. The GNN model achieved high Cohen’s kappa coefficients when classifying buildings into different categories. The study demonstrates three core novel contributions: building classification across multiple countries using multi-source data; GNN models that consider contextual information about building neighborhoods improve predictive performance; and training with localized subgraphs improves performance for building classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about trying to figure out what type of buildings are in different places across Europe. They used a special kind of computer model called a graph neural network (GNN) to look at pictures of buildings and try to guess what they’re used for. The GNN was really good at guessing! They found that it’s possible to use information about the shapes of buildings, where they are in relation to other things, and even what countries they’re in to figure out what kind of building it is. This is important because we need this kind of information to make our homes and communities more energy efficient. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network
