2023 - Research.com Computer Science in Austria Leader Award
His primary areas of study are Data mining, Artificial intelligence, Image segmentation, Remote sensing and Geographic information system. His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition. His Image processing and Image resolution study in the realm of Computer vision interacts with subjects such as Object-oriented programming and National Agriculture Imagery Program.
His Image segmentation study incorporates themes from Contextual image classification and Change detection. His Remote sensing research incorporates themes from Pixel, Aerosol and Moderate-resolution imaging spectroradiometer. His research integrates issues of Landscape planning, Sustainable management, Geospatial analysis and Data science in his study of Geographic information system.
Remote sensing, Artificial intelligence, Geographic information system, Landslide and Cartography are his primary areas of study. His Remote sensing research is multidisciplinary, incorporating elements of Object and Pixel. His Artificial intelligence research includes elements of Machine learning, Computer vision and Pattern recognition.
His study in Image and Image processing is carried out as part of his Computer vision studies. His Geographic information system research incorporates elements of Environmental resource management, Data mining, Geospatial analysis and Data science. The various areas that Thomas Blaschke examines in his Data mining study include Robustness and Scale.
His main research concerns Artificial intelligence, Landslide, Landslide susceptibility, Cartography and Geographic information system. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Normalized Difference Vegetation Index. Thomas Blaschke interconnects Hazard, Ensemble forecasting and Remote sensing in the investigation of issues within Landslide.
The concepts of his Cartography study are interwoven with issues in Land cover, Land use and Natural hazard. His Geographic information system research includes themes of Statistical model and Data science. His biological study spans a wide range of topics, including Artificial neural network and Remote sensing.
Thomas Blaschke mostly deals with Ensemble forecasting, Artificial intelligence, Random forest, Landslide susceptibility and Cartography. His research in Artificial intelligence intersects with topics in Machine learning, City block and Information retrieval. His Landslide susceptibility research integrates issues from Dempster–Shafer theory, Object based and River watershed.
Thomas Blaschke has researched Cartography in several fields, including Landslide, Land use, Natural hazard, Hazard and Flood myth. Along with Volume, other disciplines of study including Data mining and Geographic information system are integrated into his research. His Geographic information system study combines topics from a wide range of disciplines, such as Object and Earth observation.
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Object based image analysis for remote sensing
Isprs Journal of Photogrammetry and Remote Sensing (2010)
Geographic Object-Based Image Analysis - Towards a new paradigm.
Thomas Blaschke;Geoffrey J. Hay;Maggi Kelly;Stefan Lang.
Isprs Journal of Photogrammetry and Remote Sensing (2014)
What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS
Thomas Blaschke;Josef Strobl.
Zeitschrift für Geoinformationssysteme (2001)
A multi-scale segmentation/object relationship modelling methodology for landscape analysis
C Burnett;Thomas Blaschke.
Ecological Modelling (2003)
Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms
John Richard Otukei;John Richard Otukei;Thomas Blaschke.
International Journal of Applied Earth Observation and Geoinformation (2010)
Object-oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications
Thomas Blaschke;Stefan Lang;Eric Lorup;Josef Strobl.
Automated classification of landform elements using object-based image analysis
Lucian Drăguţ;Thomas Blaschke.
A comparison of three image-object methods for the multiscale analysis of landscape structure
Geoffrey J. Hay;Thomas Blaschke;Danielle J. Marceau;André Bouchard.
Isprs Journal of Photogrammetry and Remote Sensing (2003)
Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications
Thomas Blaschke;Stefan Lang;Geoffrey J. Hay.
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Omid Ghorbanzadeh;Thomas Blaschke;Khalil Gholamnia;Sansar Raj Meena.
Remote Sensing (2019)
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