Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Analyzing Drone Imagery of Flooded Regions with Deep Neural Networks
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Analys av drönarbilder över översvämmade områden med djupa neurala nätverk (Swedish)
Abstract [en]

Flooding is the world’s most prevalent natural disaster, causing a large amount of fatalities and severe economical consequences each year. In this thesis, drone imagery of flooded regions has been analyzed by deep neural networks in order to facilitate disaster prevention and response. The deep neural networks have been used to do image segmentation of buildings, roads and water. Two deep learning algorithms have been compared, the instance segmentation network Mask R-CNN and the semantic segmentation network Deeplabv3+, showing that Deeplabv3+ provides better segmentation masks for this type of imagery with a mIoU score close to 0.9 for buildings and water. Moreover, two post-processing methods have been implemented to investigate if they can improve the segmentation results. The implemented methods are morphological opening and closing operations as well as fully-connected conditional random fields. The experimental results show that these post-processing tools are able to slightly improve the results from the deep neural networks.

Place, publisher, year, edition, pages
2019. , p. 59
Series
TRITA-EECS-EX ; 2019:657
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-264977OAI: oai:DiVA.org:kth-264977DiVA, id: diva2:1376296
External cooperation
GLOBHE Drones
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2020-01-17 Created: 2019-12-09 Last updated: 2020-01-17Bibliographically approved

Open Access in DiVA

fulltext(23089 kB)21 downloads
File information
File name FULLTEXT01.pdfFile size 23089 kBChecksum SHA-512
e052a8e97333ef2dbdc45c3747ce55245d3bc24ffd4075e50ae52eeae13c0718a980fe3a7b3d9b575ecd8126058d16d9611e7eb2df1672444d270bf351583276
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering and Computer Science (EECS)
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 21 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 33 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf