Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data

The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a “building-ground elevation difference model” (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction

Building Detection from Very High Resolution Remotely Sensed Imagery Using Deep Neural Networks

The past decades have witnessed a significant change in human societies with a fast pace and rapid urbanization. The boom of urbanization is contributed by the influx of people to the urban area and comes with building construction and deconstruction. The estimation of both residential and industrial buildings is important to reveal and demonstrate the human activities of the regions. As a result, it is essential to effectively and accurately detect the buildings in urban areas for urban planning and population monitoring. The automatic building detection method in remote sensing has always been a challenging task, because small targets cannot be identified in images with low resolution, as well as the complexity in the various scales, structure, and colours of urban buildings. However, the development of techniques improves the performance of the building detection task, by taking advantage of the accessibility of very high-resolution (VHR) remotely sensed images and the innovation of object detection methods. The purpose of this study is to develop a framework for the automatic detection of urban buildings from the VHR remotely sensed imagery at a large scale by using the state-of-art deep learning network. The thesis addresses the research gaps and difficulties as well as the achievements in building detection. The conventional hand-crafted methods, machine learning methods, and deep learning methods are reviewed and discussed. The proposed method employs a deep convolutional neural network (CNN) for building detection. Two input datasets with different spatial resolutions were used to train and validate the CNN model, and a testing dataset was used to evaluate the performance of the proposed building detection method. The experiment result indicates that the proposed method performs well at both building detection and outline segmentation task with a total precision of 0.92, a recall of 0.866, an F1-score of 0.891. In conclusion, this study proves the feasibility of CNN on solving building detection challenges using VHR remotely sensed imagery