Detection of informal settlements from VHR satellite images using convolutional neural networks

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)
Original languageUndefined
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Subtitle of host publication23-28 July 2017, Fort Worth Texas, USA
PublisherIEEE
Pages5169-5172
Number of pages4
ISBN (Electronic)978-1-5090-4951-6
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • computer vision
  • convolution
  • feature extraction
  • geophysical image processing
  • image classification
  • learning (artificial intelligence)
  • neural nets
  • CNNs outperform state
  • Dar es Salaam
  • VHR satellite images
  • convolutional neural networks
  • deep feature
  • different urban structure types
  • end-to-end fashion
  • high-resolution aerial
  • informal settlements
  • satellite imagery
  • spatial-contextual features
  • standard land-cover classes
  • Convolution
  • Feature extraction
  • Kernel
  • Machine learning
  • Support vector machines
  • Training
  • Image classification
  • deep learning
  • high resolution satellite imagery

Cite this

Mboga, N., Persello, C., Bergado, J. R., & Stein, A. (2017). Detection of informal settlements from VHR satellite images using convolutional neural networks. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 July 2017, Fort Worth Texas, USA (pp. 5169-5172). IEEE. https://doi.org/10.1109/IGARSS.2017.8128166
Mboga, Nicholus ; Persello, C. ; Bergado, J. R. ; Stein, A. / Detection of informal settlements from VHR satellite images using convolutional neural networks. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 July 2017, Fort Worth Texas, USA. IEEE, 2017. pp. 5169-5172
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Mboga, N, Persello, C, Bergado, JR & Stein, A 2017, Detection of informal settlements from VHR satellite images using convolutional neural networks. in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 July 2017, Fort Worth Texas, USA. IEEE, pp. 5169-5172. https://doi.org/10.1109/IGARSS.2017.8128166

Detection of informal settlements from VHR satellite images using convolutional neural networks. / Mboga, Nicholus; Persello, C.; Bergado, J. R.; Stein, A.

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 July 2017, Fort Worth Texas, USA. IEEE, 2017. p. 5169-5172.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

TY - CHAP

T1 - Detection of informal settlements from VHR satellite images using convolutional neural networks

AU - Mboga, Nicholus

AU - Persello, C.

AU - Bergado, J. R.

AU - Stein, A.

PY - 2017/7/1

Y1 - 2017/7/1

KW - computer vision

KW - convolution

KW - feature extraction

KW - geophysical image processing

KW - image classification

KW - learning (artificial intelligence)

KW - neural nets

KW - CNNs outperform state

KW - Dar es Salaam

KW - VHR satellite images

KW - convolutional neural networks

KW - deep feature

KW - different urban structure types

KW - end-to-end fashion

KW - high-resolution aerial

KW - informal settlements

KW - satellite imagery

KW - spatial-contextual features

KW - standard land-cover classes

KW - Convolution

KW - Feature extraction

KW - Kernel

KW - Machine learning

KW - Support vector machines

KW - Training

KW - Image classification

KW - deep learning

KW - high resolution satellite imagery

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Mboga N, Persello C, Bergado JR, Stein A. Detection of informal settlements from VHR satellite images using convolutional neural networks. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 July 2017, Fort Worth Texas, USA. IEEE. 2017. p. 5169-5172 https://doi.org/10.1109/IGARSS.2017.8128166