Abstract
Abstract. With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow online analysis of building structures in city models given oblique aerial image sequences, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery. We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset. We achieve an average precision (AP) of about 80 % for the task of building extraction on a selected evaluation dataset. Our evaluation focuses on both dataset-specific learning and transfer learning. Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial image sequences in real-time. We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries. In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery.
Highlights
In recent years, more and more attention has been paid to the automated analyses of urban areas due to an increase in urbanization and the need for more efficient urban planing and sustainable development
Before presenting the results achieved by the Faster R-convolutional neural networks (CNNs) and our algorithm for depth estimation, we introduce the datasets used for training and evaluation
4.2.2 Results We have evaluated the performance of the Faster R-CNN to detect buildings on the validation subsets of the Cityscapes and ISPRS datasets as well as the generated GoogleEarth dataset
Summary
More and more attention has been paid to the automated analyses of urban areas due to an increase in urbanization and the need for more efficient urban planing and sustainable development. While the consideration of urban trees in planning processes can provide measurable economic, environmental, social and health benefits (Kelly, 2011), the monitoring and analysis of the given buildings e.g. allows to create and update city models (Kolbe, 2009), finding suitable roof planes for solar energy installations (Schuffert et al, 2015) and reasoning about a diversity of processes. The basis for such automated analyses is typically given with data acquired from aerial platforms. To foster research on both of these issues, the ISPRS benchmark on urban object classification and 3d building reconstruction (Rottensteiner et al, 2012) has been initialized and addressed by a diversity of approaches
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