Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change” |
| Pages | 631-637 |
| Number of pages | 7 |
| Publication status | Published - 2018 |
| Event | ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change - Delft, Netherlands Duration: 1 Oct 2018 → 5 Oct 2018 |
Publication series
| Name | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
|---|---|
| Publisher | International Society for Photogrammetry and Remote Sensing |
| Volume | XLII-4 |
| ISSN (Print) | 1682-1750 |
Abstract
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.
Keywords
- Cartography, Deep learning, Generalization
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change”. 2018. p. 631-637 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives; Vol. XLII-4).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -