Corpus ID: 236428233

Continental-Scale Building Detection from High Resolution Satellite Imagery

  title={Continental-Scale Building Detection from High Resolution Satellite Imagery},
  author={Wojciech Sirko and Sergii Kashubin and Marvin Ritter and Abigail Annkah and Yasser Salah Edine Bouchareb and Yann Dauphin and Daniel Keysers and Maxim Neumann and Moustapha Ciss{\'e} and John Quinn},
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes. Such information can be particularly useful in developing regions where alternative data sources may be scarce. In this work, we describe a model training pipeline for detecting buildings across the entire continent of Africa, using 50 cm satellite imagery. Starting with the U-Net model, widely used in satellite image analysis, we study variations in architecture, loss functions… Expand


ImageNet Large Scale Visual Recognition Challenge
The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared. Expand
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Mask R-CNN
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DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
  • Ilke Demir, K. Koperski, +6 authors R. Raskar
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2018
The DeepGlobe 2018 Satellite Image Understanding Challenge is presented, which includes three public competitions for segmentation, detection, and classification tasks on satellite images, and characteristics of each dataset are analyzed, and evaluation criteria for each task are defined. Expand