Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering

2019

IJGI

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 human operator is the benchmark, 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 traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance.

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Learning Cartographic Building Generalization with Deep Convolutional Neural Networks

Feng

Thiemann

2019

IJGI

“…Another potential approach is to use GANs (Generalized Adversarial Networks) in order to synthesize a new representation based on a given input (see e.g. Isola et al (2017); Peters and Brenner (2018)).…”

Section: Discussion and Outlook On Future Workmentioning

confidence: 99%

Building Generalization Using Deep Learning

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Feng

Thiemann

2018

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 &ndash; computer vision and cartography &ndash; 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.

“…This information has been exploited in another research work to synthesize realistic visualizations from Lidar data projections. In order to do so, a conditional GAN was trained with pairs of Lidar and camera-data, including a time-stamp (Peters and Brenner 2018). In this way it is possible to not only generate realistic views (Figure 4), but also realistic views at different times of the year, e.g.…”

Section: Acquisition Of Environmental Informationmentioning

confidence: 99%

Analysis of mobility data – A focus on Mobile Mapping Systems

Geo-spatial Information Science

2020

The increasing availability of devices to capture the position of moving objects (and other environmental information) leads to a very large amount and variety of mobility data. In order to obtain important information about the objects, their behavior or the environment of the objects, an automatic analysis is required. This article highlights current research questions in the context of the analysis of mobility data and presents them on the basis of work carried out at the Institute of Cartography and Geoinformatics (ikg) at Leibniz University of Hannover, Germany. A focus is put on the analysis and exploitation of information from Mobile Mapping vehicles.