Doctoral Studentship in Smart Cities and Urban Planning. KTH Royal Institute of Technology. Division of Urban and Regional Studies.

-

Doctoral Studentship in Smart Cities and Urban Planning

KTH Royal Institute of Technology
Division of Urban and Regional Studies

Deadline: 28 May 2020

Municipal governments are increasingly using information and communication technologies (ICT) to optimise collective public services while providing an improved quality of life for urban residents. Urban planners have largely played a peripheral role in these digitalisation activities but this is beginning to change as smart cities become a common approach to urban development. This doctoral studentship focuses on the ways that urban planners are engaging with smart cities projects and programmes and how this is changing their influence on the governance of cities. 
The position is based in the Division of Urban and Regional Studies at the KTH Royal Institute of Technology in central Stockholm. The doctoral student will be supervised by Andrew Karvonen.
Further information and application instructions:






🌍
🌏
🌎
🌐
😀
👍🏾
#Globe
#Earth
#Map
#Decision Making
#Cartography
#Cartographer
#Geography
#Geographer
#GIS
#Remote_Sensing


....


Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc

Comments

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Grey level thresholding. Level slicing. Contrast stretching lo p

-
Image
Grey level thresholding. Level slicing. Contrast stretching. Image enhancement Lillesand and Kiefer (1994) explained the goal of image enhancement procedures is to improve the visual interpretability of any image by increasing the apparent distinction between the features in the scene. This objective is to create "new" image from the original image in order to increase the amount of information that can be visually interpreted from the data. Enhancement operations are normally applied to image data after the appropriate restoration procedures have been performed. Noise removal, in particular, is an important precursor to most enhancements. In this study, typical image enhancement techniques are as follows: Grey level thresholding Grey level thresholding is a simple lookup table, which partitions the gray levels in an image into one or two categories - those below a user-selected threshold and those above. Thresholding is one of many methods for creating a binary mask for an i
Read more

Spatial filtering, Remote Sensing. Low-pass filter. High-pass filter

-
Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture. It refers to the frequency of the variations in tone that appear in an image. "Rough" textured areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while "smooth" areas with little variation in tone over several pixels, have low spatial frequencies. A common filtering procedure involves moving a 'window' of a few pixels in dimension (e.g. 3x3, 5x5, etc.) over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time and the ca
Read more