Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a new 3D point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. It can be used, among others, to train semantic segmentation networks or to learn the structure of buildings and the geometric model construction. Further details about RoofN3D and the developed data preparation framework, which enables the automatic derivation of training data, are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to unstructured and not gridded 3D point cloud data are presented.

<p><strong>Abstract.</strong> Virtual city models are important for many applications such as urban planning, virtual and augmented reality, disaster management, and gaming. Urban features such as buildings, roads, and trees are essential components of these models and are subject to frequent change and alteration. It is laborious to manually build and update virtual city models, due to a large number of instances and temporal changes on such features. The increase of publicly available spatial data provides an important source for pipelines that automate virtual city model generation. The large quantity of data also opens an opportunity to use Deep Learning (DL) as a technique that minimizes the need for expert domain knowledge. In addition, many Deep Learning models calculations can be parallelized on modern hardware such as graphical processing units, which reduces the computation time substantially.</p><p>We explore the opportunity of using publicly available data in computing multiple thematic data layers from Digital Surface Models (DSMs) using an automatic pipeline that is powered by a semantic segmentation network. To evaluate this design, we implement our pipeline using multiple Convolutional Neural Networks (CNN) with an encoder-decoder architecture. We produce a variety of two and three-dimensional thematic data. We focus our evaluation on the pipeline’s ability to produce accurate building footprints. In our experiments we vary the depths, the number of input channels and data resolutions of the evaluated networks. Our experiments process public data that is provided by New York City.</p>

Machine learning methods, in particular those based on deep learning, have gained in importance through the latest development of artificial intelligence and computer hardware. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a three-dimensional (3D) point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. The details about RoofN3D and the developed framework to automatically derive such training data are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to 3D point cloud data are presented. Finally, we exemplarily demonstrate how the provided data can be used to classify building roofs with the PointNet framework.

<p><strong>Abstract.</strong> The abundance of high-quality satellite images is salutary for many activities but raises also privacy and security concerns. Manually obfuscating areas subject to privacy issues by applying locally pixelization techniques leads to undesirable discontinuities in the visual appearance of the depicted scenes. Alternatively, automatically generated photorealistic fillers can be used to obfuscate sensitive information while preserving the original visual aspect of high-resolution aerial images.</p><p>Recent advances in the field of Deep Learning (DL) enable to synthesize high-quality image data. Particularly, generative models such as Generative Adversarial Networks (GANs) can be used to produce images that can be perceived as photorealistic even by human examiners. Additionally, Conditional Generative Adversarial Networks (cGANs) allow control over the image generation process and results. These developments give the opportunity to generate photorealistic fillers for the purpose of privacy and security in image data used within city models while preserving the quality of the original data. However, according to our knowledge, little research has been done to explore this potential. In order to close this gap, we propose a novel framework that is designed to correspond to the mentioned end goal and produces promising results.</p>

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