姓名： Lutz Plümer
职称： 教授
电话：
Email： pluemer@swjtu.edu.cn



个人信息


Dean of the Faculty of Geosciences and Environmental Engineering South West Jiaotang University, West Park Chengdu Hi-Tech Zone Sichuan Province, PR China 611757 Professor emeritus University of Bonn Former Managing Director of the Institute for Cartography and Geoinformation Chairman of the GIS Centre of the University of Bonn Senator of the University of Bonn,Chairman of the Finding Commission for the University Council Member of the Bavarian Academy of Science
Research Fields
·Geoinformation
·Machine Learning and Artificial Intelligence for Geoinformation
·3D City Models
·Indoor Models

Education Background
·1986
University of Dortmund (Germany) / Computer Science / Diploma
(Grant of the German National Scholarship Foundation)
·1989
Promotion (PhD) at the University of Dortmund (Germany), summa cum laude
Thesis: Termination Proofs for Logic Program
·1994
Habilitation at the UniversityofBonn
Thesis: Automatic Verification of Parallel Logic Programs
·1997 – 1999
Professor at the Institute for Environmental Sciences /UniversityofVechta(Germany)
·1999 - 2016
Full Professor for Geoinformation, Institute of Geodesy and Geoinformation / University of Bonn (Germany)

Recent Publications
·Dehbi, Y, Haunert, J, and Plümer, L (2017).
Stochastic and geometric reasoning for indoor building models with electric installations - bridging the gap between GIS and BIM
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W5:33–39.
·Dehbi, Y, Loch-Dehbi, S, and Plümer, L (2017).
Parameter estimation and model selection for indoor models based on sparse observations
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W4:303–310.
·Schmitter, P, Steinrücken, J, R?mer, C, Ballvora, A, Léon, J, Rascher, U, and Plümer, L (2017).
Unsupervised domain adaptation for early detection of drought stress in hyperspectral images
ISPRS\ Journal of Photogrammetry and Remote Sensing , 131:65 - 76.
·Dehbi, Y, Hadiji, F, Gr?ger, G, Kersting, K, and Plümer, L (2017).
Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction
Transactions in GIS, 21(1):134-150.
·Loch-Dehbi, S, Dehbi, Y, and Plümer, L (2017).
Estimation of 3D Indoor Models with Constraint Propagation and Stochastic Reasoning in the Absence of Indoor Measurements
ISPRS International Journal of Geo-Information, 6(3, article number = 90).
·Behmann, J, Hendriksen, K, Müller, U, Büscher, W, and Plümer, L (2016).
Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors
GeoInformatica, 20(4):693-714.
·Behmann, J, Mahlein, A, Paulus, S, Dupuis, J, Kuhlmann, H, Oerke, E, and Plümer, L (2016).
Generation and application of hyperspectral 3D plant models: methods and challenges
Machine Vision and Applications, 27(5):611-624.
·Dehbi, Y, Gr?ger, G, and Plümer, L (2016).
Identification and modelling of translational and axial symmetries and their hierarchical structures in building footprints by formal grammars
Transactions in GIS, 20(5):645-663.
·Dehbi, Y, Staat, C, Mandtler, L, and Plümer, L (2016).
Incremental refinement of facade models with attribute grammar from 3D point clouds
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-3:311-316.
·Loch-Dehbi, S, Dehbi, Y, Gr?ger, G, and Plümer, L (2016).
Prediction of building floorplans using logical and stochastic reasoning based on sparse observations
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W1:265-270.
·Roscher, R, Behmann, J, Mahlein, A, Dupuis, J, Kuhlmann, H, and Plümer, L (2016).
Detection of disease symptoms on hyperspectral 3D plant models
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7:89-96.
·Behmann, J, Mahlein, A, Paulus, S, Kuhlmann, H, Oerke, E, and Plümer, L (2015).
Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping
\ISPRS\ Journal of Photogrammetry and Remote Sensing, 106(0):172-182.
·Behmann, J, Mahlein, A, Rumpf, T, R?mer, C, and Plümer, L (2015).
A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
Precision Agriculture, 16(3):239-260.
·Loch-Dehbi, S and Plümer, L (2015).
Predicting building fa?ade structures with multilinear Gaussian graphical models based on few observations
Computers, Environment and Urban Systems, 54:68–81.