M
Monika Sester
Researcher at Leibniz University of Hanover
Publications - 247
Citations - 4430
Monika Sester is an academic researcher from Leibniz University of Hanover. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 33, co-authored 216 publications receiving 3741 citations. Previous affiliations of Monika Sester include University of Stuttgart & Lund University.
Papers
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Journal ArticleDOI
Information from imagery: ISPRS scientific vision and research agenda
Jun Chen,Ian Dowman,Songnian Li,Zhilin Li,Marguerite Madden,Jon P. Mills,Nicolas Paparoditis,Franz Rottensteiner,Monika Sester,Charles K. Toth,John Trinder,Christian Heipke +11 more
TL;DR: The significant challenges currently facing ISPRS and its communities are examined, such as providing high-quality information, enabling advanced geospatial computing, and supporting collaborative problem solving.
Journal ArticleDOI
Landmark hierarchies in context
TL;DR: A computational model is proposed for the generation of a hierarchy of one of these elements of the city—landmarks—and it is demonstrated that a set of filter rules applied on this hierarchy derives distinguishing route descriptions from spatial context.
Book ChapterDOI
Continuous Generalization for Visualization on Small Mobile Devices
Monika Sester,Claus Brenner +1 more
TL;DR: This work automatically decomposes the generalization of an object into a sequence of elementary steps, which leads to smooth transitions between different object representations and is useful for incremental transmission of maps through limited bandwidth channels.
Journal ArticleDOI
Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos
Yu Feng,Monika Sester +1 more
TL;DR: High quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos, and events are detected through spatiotemporal clustering and visualized together with these high qualityewitnesses in a web map application.
Journal ArticleDOI
Quality assessment of OpenStreetMap data using trajectory mining
Anahid Basiri,Mike Jackson,Pouria Amirian,Amir Pourabdollah,Monika Sester,Adam C. Winstanley,Terry Moore,Lijuan Zhang +7 more
TL;DR: This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users so that certain characteristics of user trajectories are directly linked to the type of feature.