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Exploring the Potential of Vegetation Indices for Urban Tree Segmentation in Street View Images
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  • Tito Arevalo-Ramirez ,
  • Anali Alfaro ,
  • José M. Saavedra ,
  • Matías Recabarren ,
  • Mauricio Ponce-Donoso ,
  • José Delpiano
Tito Arevalo-Ramirez
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Anali Alfaro
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José M. Saavedra
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Matías Recabarren
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Mauricio Ponce-Donoso
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José Delpiano
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Urban forests play a crucial role in the development of cities because of the urban ecosystem services they provide. Previous works have alleviated urban forest monitoring by discriminating tree species and performing tree inventories using street view images and convolutional neural networks. However, the characterization of trees from street-view images remains a challenging task. Determining tree structural parameters has been limited because of inaccurate tree segmentation caused by combined, occluded, or leaf-off trees. Therefore, the current work evaluates the potential of vegetation indices derived from red, green, blue, and synthesized near-infrared and red-edge spectral bands for urban tree segmentation. In particular, we attempt to show whether or not vegetation indices add relevant information to deep neural segmentation networks when there are low fine-tuning training samples. A conditional adversarial network generates red-edge and near-infrared images in urban environments, which retrieve an average structural similarity index of 0.86 and 0.81, respectively. Furthermore, we note that by using appropriate multispectral vegetation indices, one can boost the average intersection over the union between 5.07 % to 13.7 %. Specifically, we suggest the SegFormer segmentation network pre-trained with the CityScapes dataset and Red Edge Modified Simple Ration index for improving urban tree segmentation. However, if no multispectral data is available, the DeepLabV3 network pre-trained with the ADE20k dataset is suggested because it could achieve the best RGB outcomes average IoU value of 0.671.