What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
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.
His most cited work include:
- Crowdsourcing geospatial data (325 citations)
- An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning (302 citations)
- EVALUATION OF AUTOMATIC ROAD EXTRACTION (198 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (55.26%)
- Computer vision (39.10%)
- Remote sensing (20.30%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (55.26%)
- Convolutional neural network (6.02%)
- Computer vision (39.10%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Classification of land cover and land use based on convolutional neural networks (21 citations)
- Modelling of buildings from aerial LiDAR point clouds using TINs and label maps (16 citations)
- CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
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.
