Damage Analysis of Grassland from Aerial Images Applying Convolutional Neural Networks

Maximilian Johenneken, Ahmad Drak, Rainer Herpers
Accepted for publication The 28th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2020) - sep 2020

Abstract

Damage to grasslands is mainly caused by wild boar during foraging. Farmers in Germany thereby register yield losses and expenses for damage repair. This contribution analyzes the acquisition and processing of aerial images to orthomosaics and image segmentation to perform spatial measurements of damaged patches in grasslands. A sample set of manually annotated orthomosaics is analyzed. Preliminary classification results applying a convolutional neural network approach to segment damaged patches are presented. First results show the applicability of the applied methods in the detection of damage caused by wild boar and suggest that other damage causes (e.g., mole damage) should be considered to improve results.

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BibTex references

@InProceedings{JDH20,
  author       = {Johenneken, Maximilian and Drak, Ahmad and Herpers, Rainer},
  title        = {Damage Analysis of Grassland from Aerial Images Applying Convolutional Neural Networks},
  booktitle    = {Accepted for publication The 28th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2020)},
  month        = {sep},
  year         = {2020},
  address      = {Hvar, Croatia},
  keywords     = {UAV images; grassland; remote sensing; orthomosaics; convolutional neural networks; wild boar},
}

Other publications in the database

» Maximilian Johenneken
» Ahmad Drak
» Rainer Herpers