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Theses and Projects: Difference between revisions
→* New! Socioecomonic analysis based on spatiotemporal and linguistic analysis on microblogging data
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Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de] | Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de] | ||
=== | === [Occupied] Image-to-Image Translation of Different Nightlight Image Types (B/M/P) === | ||
Nightlight intensities have been proven to be a good indicator for socio-economic status. However, for long-term temporal analyses their use can be challenging, as different satellites for sensing nightlight intensities operated at different times (DMSP OLS 1992-2014 and VIIRS 2012-2023). Both types differ not only in resolution, but there is also a big discrepancy in the optical appearance and value ranges. To obtain consistent nightlight images for temporal analysis, Image-to-Image Translation methods shall be used in this project/thesis for the conversion between both types. Finally the performance of the translated and original nightlight images for a regression on socio-economic indicators shall be evaluated. | Nightlight intensities have been proven to be a good indicator for socio-economic status. However, for long-term temporal analyses their use can be challenging, as different satellites for sensing nightlight intensities operated at different times (DMSP OLS 1992-2014 and VIIRS 2012-2023). Both types differ not only in resolution, but there is also a big discrepancy in the optical appearance and value ranges. To obtain consistent nightlight images for temporal analysis, Image-to-Image Translation methods shall be used in this project/thesis for the conversion between both types. Finally the performance of the translated and original nightlight images for a regression on socio-economic indicators shall be evaluated. | ||
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Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P) | Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P) | ||
=== * ''' | === * '''[Closed]''' Socioecomonic analysis based on spatiotemporal and linguistic analysis on microblogging data === | ||
Identifying the socioeconomic status (SES) of users in social media like Twitter or Weibo is useful e.g., for digitized advertisements and social policies. This study aims to collect profiles of Twitter users on selected topics such as culture or foreign language learning, extract the temporal, spatial and linguistic features, and compare different classification algorithms (e.g., decision tree, random forest, na\"{i}ve Bayes, deep learning, and Gaussian processes classifier) to predict the socioeconomic status. | Identifying the socioeconomic status (SES) of users in social media like Twitter or Weibo is useful e.g., for digitized advertisements and social policies. This study aims to collect profiles of Twitter users on selected topics such as culture or foreign language learning, extract the temporal, spatial and linguistic features, and compare different classification algorithms (e.g., decision tree, random forest, na\"{i}ve Bayes, deep learning, and Gaussian processes classifier) to predict the socioeconomic status. | ||
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Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P) | Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P) | ||
=== [Closed] Super resolution technique for efficient video delivery === | === [Closed] Super resolution technique for efficient video delivery === | ||