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TEAM 3: Improving Open Land Use Map by using Satellite Data

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TEAM 3: Improving Open Land Use Map by using Satellite Data

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TEAM 3: Improving Open Land Use Map by using Satellite Data

  1. 1. Open Land Use Map improvement based on satellite imagery Dmitrij Kožuch, Belaid Khemnou Open Land Use Map
  2. 2. • In the light of increased availability of satellite data nowadays (for instance, data from Sentinel missions which is very easy accessible from Copernicus programme hub) it becomes an excellent data source for our Open Land Use Map, especially relevant for the regions where land surveys are rare or their data is not available Background
  3. 3. Background • One such area were there are not so much data sources, from which land use can be derived is African continent • Last year it was an attempt to create Open Land Use Map of Africa based on available data sources – Africover, Open Street Map, local data , but the major problem was that still big areas are not covered in this datasets and also not all the time it is possible to derive land use category from land cover category
  4. 4. Background • Open Land Use Map of Africa visualization:
  5. 5. Background • Open Land Use Map of Africa visualization:
  6. 6. Background • Problems also exist in Europe. For example land use attribute is in the Czech cadastral data. Each land parcel has this attribute. Nevertheless often this attribute does not get updated although the land use of parcel changes. So there are many mistakes in this database.
  7. 7. Background • Nowadays there is very big leap in development of technologies taking place. New fields of computer science such as machine learning emerging. They bring new tools and methods for image classification also, that could be very helpful for our purpose.
  8. 8. Aim • The aim of this task then is to develop an algorithm for creating and regularly updating Open Land Use map based on satellite imagery.
  9. 9. Data • Satellite imagery (Sentinel 2) • Global Urban footprint dataset, Africover for masking out non-urban areas • Open Land Use map to select training/validation set
  10. 10. Tools • TensorFlow machine learning framework • Available as python/C++/Go library
  11. 11. Process • Select suitable training and validation dataset based on Sentinel 2 and Open Street Map datasets • Run convolutional neural network deep learning to detect different land uses in Tensor Flow framework.
  12. 12. Samples
  13. 13. Samples
  14. 14. Convolutional Neural Network Neural network with many convolutional layers
  15. 15. Convolutional Neural Network Convolution filter
  16. 16. Convolutional Neural Network Convolution filter
  17. 17. Current goals • Collect big amount samples of land use objects from Sentinel 2 images based on OSM • Train and validate classification model
  18. 18. Thanks for your attention!

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