TY - GEN

T1 - Building generalization using deep learning

AU - Sester, Monika

AU - Feng, Yu

AU - Thiemann, Frank

PY - 2018

Y1 - 2018

N2 - Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the benchmark is the human operator, who is able to design an aesthetic and correct representation of the physical reality. Deep Learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform the traditional computer vision methods. In both domains - computer vision and cartography - humans are able to produce a solution; a prerequisite for this is, that there is the possibility to generate many training examples for the different cases. Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. An advantage of this task is the fact that many training data sets are available from given map series. The approach is a first attempt using an existing network. In the paper, the details of the implementation will be reported, together with an in depth analysis of the results. An outlook on future work will be given.

AB - Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the benchmark is the human operator, who is able to design an aesthetic and correct representation of the physical reality. Deep Learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform the traditional computer vision methods. In both domains - computer vision and cartography - humans are able to produce a solution; a prerequisite for this is, that there is the possibility to generate many training examples for the different cases. Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. An advantage of this task is the fact that many training data sets are available from given map series. The approach is a first attempt using an existing network. In the paper, the details of the implementation will be reported, together with an in depth analysis of the results. An outlook on future work will be given.

KW - Cartography

KW - Deep learning

KW - Generalization

UR - http://www.scopus.com/inward/record.url?scp=85056204011&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLII-4-565-2018

DO - 10.5194/isprs-archives-XLII-4-565-2018

M3 - Conference contribution

AN - SCOPUS:85056204011

T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

SP - 631

EP - 637

BT - Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change”

T2 - ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change

Y2 - 1 October 2018 through 5 October 2018

ER -