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
More filters
Journal ArticleDOI
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges
Songnian Li,Suzana Dragićević,Francesc Antón Castro,Monika Sester,Stephan Winter,Arzu Çöltekin,Christopher Pettit,Bin Jiang,James Haworth,Alfred Stein,Tao Cheng +10 more
TL;DR: The International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisited the existing geospatial data handling methods and theories.
Journal ArticleDOI
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges
Songnian Li,Suzana Dragićević,François Anton,Monika Sester,Stephan Winter,Arzu Çöltekin,Christopher Pettit,Bin Jiang,James Haworth,Alfred Stein,Tao Cheng +10 more
TL;DR: In this article, a position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging gespatial big data, synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.
Proceedings ArticleDOI
Spatial information retrieval and geographical ontologies an overview of the SPIRIT project
Christopher B. Jones,Ross S. Purves,Anne Ruas,Mark Sanderson,Monika Sester,M.J. van Kreveld,Robert Weibel +6 more
TL;DR: A brief survey of existing facilities for geographical information retrieval on the web is provided, before describing a set of tools and techniques that are being developed in the project SPIRIT : Spatially-Aware Information Retrieval on the Internet.
Journal ArticleDOI
Optimization approaches for generalization and data abstraction
TL;DR: An overview on current approaches for the automation of generalization and data abstraction is given, and solutions for three generalization problems based on optimization techniques based on Neural Network techniques are presented.
Generalization based on least squares adjustment
TL;DR: The paper presents solutions for generalization problems using least squares adjustment theory, a well known general framework to determine unknown parameters based on given observations, and demonstrates the validity of this approach to the simplification of building ground plans and the displacement of arbitrary cartographic objects.