Member of the European Academy of Sciences and Arts
Christian Heipke mainly investigates Remote sensing, Photogrammetry, Artificial intelligence, Computer vision and Terrain. His Remote sensing research incorporates themes from Tree and Point cloud. In his study, Lidar is strongly linked to Feature selection, which falls under the umbrella field of Point cloud.
His work deals with themes such as Data mining, Geospatial analysis, Relevance, Scale and Field, which intersect with Photogrammetry. His research combines Algorithm and Artificial intelligence. His study in Terrain is interdisciplinary in nature, drawing from both Image, Thresholding and Rule-based system.
Christian Heipke mostly deals with Artificial intelligence, Computer vision, Remote sensing, Photogrammetry and Pattern recognition. In his works, Christian Heipke conducts interdisciplinary research on Artificial intelligence and Matching. His Remote sensing study combines topics in areas such as Pixel and Terrain.
His Photogrammetry study frequently draws parallels with other fields, such as Computer graphics. He has included themes like Data mining and Feature in his Pattern recognition study. His Data mining research includes themes of Image segmentation, Data set, Database and Geographic information system.
Artificial intelligence, Convolutional neural network, Computer vision, Deep learning and Pattern recognition are his primary areas of study. Christian Heipke integrates many fields in his works, including Artificial intelligence and Matching. His biological study spans a wide range of topics, including Object, Spatial database and Geospatial analysis.
His research on Computer vision often connects related topics like Simulated annealing. His Deep learning research includes elements of Change detection, Training set, Remote sensing, Photogrammetry and Component. His work in the fields of Feature matching overlaps with other areas such as Noise.
Christian Heipke focuses on Artificial intelligence, Computer vision, Convolutional neural network, Deep learning and Photogrammetry. Christian Heipke merges many fields, such as Artificial intelligence and Kernel density estimation, in his writings. Christian Heipke interconnects Simulated annealing and Ellipse in the investigation of issues within Computer vision.
His study explores the link between Convolutional neural network and topics such as Geospatial analysis that cross with problems in Aerial photography. His study ties his expertise on Remote sensing together with the subject of Photogrammetry. His work in RGB color model covers topics such as Image quality which are related to areas like Orientation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Crowdsourcing geospatial data
Isprs Journal of Photogrammetry and Remote Sensing (2010)
An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning
Harri Kaartinen;Juha Hyyppä;Xiaowei Yu;Mikko Vastaranta.
Remote Sensing (2012)
EVALUATION OF AUTOMATIC ROAD EXTRACTION
C. Heipke;H. Mayer;C. Wiedemann;O. Jamet.
Relevance assessment of full-waveform lidar data for urban area classification
Clément Mallet;Frédéric Bretar;Michel Roux;Uwe Soergel.
Isprs Journal of Photogrammetry and Remote Sensing (2011)
Joint 3d Estimation of Vehicles and Scene Flow
Moritz Menze;Christian Heipke;A. Geiger.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2015)
Topography of Mars from global mapping by HRSC high-resolution digital terrain models and orthoimages: Characteristics and performance
K. Gwinner;F. Scholten;Frank Preusker;S. Elgner.
Earth and Planetary Science Letters (2010)
Automation of interior, relative, and absolute orientation
Isprs Journal of Photogrammetry and Remote Sensing (1997)
Integration of heterogeneous geospatial data in a federated database
Matthias Butenuth;Guido v. Gösseln;Michael Tiedge;Christian Heipke.
Isprs Journal of Photogrammetry and Remote Sensing (2007)
Discrete Optimization for Optical Flow
Moritz Menze;Christian Heipke;Andreas Geiger.
german conference on pattern recognition (2015)
MULTIPLE-MODEL BASED VERIFICATION OF JAPANESE ROAD DATA
M. Ziems;H. Fujimura;Christian Heipke;Franz Rottensteiner.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2010)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: