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pictorial-maps-keras

https://github.com/narrat3d/pictorial-maps-keras

Science Score: 65.0%

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    Found 3 DOI reference(s) in README
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    Organization narrat3d has institutional domain (narrat3d.ethz.ch)
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    Low similarity (11.1%) to scientific vocabulary
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Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

Classification of (non-)maps and (non-)pictorial maps

This is code for the article Detection of Pictorial Map Objects with Convolutional Neural Networks. Visit the project website for more information.

maps

Image sources: Physical Map of the World, Tampa-Bay Aerial View Map

Installation

Inference

  • Adjust models_folder in config.py
  • Download trained models and place them inside the models folder

Maps vs. Non-maps

  • Run classify_maps.py <input folder with images> <output folder for map and non-map images>

Pictorial maps vs. non-pictorial maps

  • Run classifypictorialmaps.py <input folder with map images> <output folder for pictorial map and non-pictorial map images>

Training

  • Adjust datafolder and logfolder in config.py
  • Download training data and place them into the data folder
  • Optionally adjust properties like models (e.g. modelnames = ["Xception"]), number of runs (e.g. runnrs = ["1st"]), image input options (e.g. mapsnonmapscropnames = ["resize"]) in config.py
  • Run training.py to train the maps vs. non-maps classifier (uncomment lines in main method to train models to classify pictorial maps)

Evaluation

  • Run prediction.py to calculate scores for correctly classifying maps and non-maps (uncomment lines for pictorial and non-pictorial maps)
  • Run evaluation.py to calculate accuracies and find wrong predictions (optionally adjust the threshold)
  • Run evaluationroccurve.py to create a ROC curve plot from the predictions

Citation

Please cite the following article when using this code: @article{schnuerer2021detection, author = {Raimund Schnürer, René Sieber, Jost Schmid-Lanter, A. Cengiz Öztireli and Lorenz Hurni}, title = {Detection of Pictorial Map Objects with Convolutional Neural Networks}, journal = {The Cartographic Journal}, volume = {58}, number = {1}, pages = {50-68}, year = {2021}, doi = {10.1080/00087041.2020.1738112} }

© 2019-2020 ETH Zurich, Raimund Schnürer

Owner

  • Name: narrat3d
  • Login: narrat3d
  • Kind: organization
  • Email: schnuerer@ethz.ch

Codebase for the doctoral project "Storytelling with Animated Interactive Objects in Real-time 3D Maps"

Citation (CITATION.cff) 

cff-version: 1.2.0
message: Please cite the following works when using this code.
preferred-citation:
  authors:
    - family-names: Schnürer
      given-names: Raimund
    - family-names: Sieber
      given-names: René
    - family-names: Schmid-Lanter
      given-names: Jost
    - family-names: Öztireli
      given-names: A. Cengiz
    - family-names: Hurni
      given-names: Lorenz
  doi: 10.1080/00087041.2020.1738112
  identifiers:
    - type: doi
      value: 10.1080/00087041.2020.1738112
    - type: url
      value: https://doi.org/10.1080/00087041.2020.1738112
    - type: other
      value: urn:issn:0008-7041
  title: Detection of Pictorial Map Objects with Convolutional Neural Networks
  url: https://doi.org/10.1080/00087041.2020.1738112
  date-published: 2020-09-11
  year: 2021
  month: 8
  issn: 0008-7041
  issue: '1'
  journal: The Cartographic Journal
  languages:
    - en
  start: '50'
  end: '68'
  type: article
  volume: '58'

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Dependencies

requirements.txt pypi
  • Pillow ==5.1.0
  • h5py ==2.7.1
  • matplotlib ==2.2.2
  • scikit-learn ==0.19.1
  • tensorflow ==1.10.0
  • tensorflow-gpu ==1.10.0