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- 摘要
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Accurate Building Detection in VHR Remote Sensing Images Using Geometric Saliency
摘要： This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR). Inspired by the observation that buildings are always more distinguishable in geometries than in texture or spectral, we propose a new geometric building index (GBI) for accurate building detection, which relies on the geometric saliency of building structures. The geometric saliency of buildings is derived from a mid-level geometric representation based on meaningful junctions that can locally describe anisotropic geometrical structures of images. The resulting GBI is measured by integrating the derived geometric saliency of buildings. Experiments on three public datasets demonstrate that the proposed GBI achieves very promising performance, and meanwhile shows impressive generalization capability.
关键词： remote sensing image,geometric saliency,junction,Building detection
更新于2025-09-23 15:22:29
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A greyscale voxel model for airborne lidar data applied to building detection
摘要： The existing binary voxel model algorithm for 3D building detection (3BD) from airborne lidar cannot distinguish between connected buildings and non-buildings. As a result, a greyscale voxel structure model, using the discretised mean intensity of lidar points, is presented to support subsequent building detection in areas where buildings are adjacent to non-buildings but with different greyscales. The resulting 3BD algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds, labelling them and their 3D connected regions as rooftop voxels. Then voxels which fall into buffers and possess similar greyscales to that of the corresponding building outline are assigned as building facades. The results for detected buildings are evaluated using lidar data with different densities and demonstrate a high rate of success.
关键词： lidar,greyscale,voxel,building detection,point cloud,intensity
更新于2025-09-23 15:22:29
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2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)
摘要： In this study, a deep learning (DL)-based approach is proposed for the detection and reconstruction of buildings from a single aerial image. The pre-required knowledge to reconstruct the 3D shapes of buildings, including the height data as well as the linear elements of individual roofs, is derived from the RGB image using an optimized multi-scale convolutional–deconvolutional network (MSCDN). The proposed network is composed of two feature extraction levels to ?rst predict the coarse features, and then automatically re?ne them. The predicted features include the normalized digital surface models (nDSMs) and linear elements of roofs in three classes of eave, ridge, and hip lines. Then, the prismatic models of buildings are generated by analyzing the eave lines. The parametric models of individual roofs are also reconstructed using the predicted ridge and hip lines. The experiments show that, even in the presence of noises in height values, the proposed method performs well on 3D reconstruction of buildings with di?erent shapes and complexities. The average root mean square error (RMSE) and normalized median absolute deviation (NMAD) metrics are about 3.43 m and 1.13 m, respectively for the predicted nDSM. Moreover, the quality of the extracted linear elements is about 91.31% and 83.69% for the Potsdam and Zeebrugge test data, respectively. Unlike the state-of-the-art methods, the proposed approach does not need any additional or auxiliary data and employs a single image to reconstruct the 3D models of buildings with the competitive precision of about 1.2 m and 0.8 m for the horizontal and vertical RMSEs over the Potsdam data and about 3.9 m and 2.4 m over the Zeebrugge test data.
关键词： convolutional neural networks,deep learning,building reconstruction,building detection,depth prediction
更新于2025-09-12 10:27:22
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Extracting Accurate Building Information from Off-Nadir VHR Images
摘要： This research demonstrates the applicability of the improved algorithm for generating LoS-DSM elevation data through an elevation-based building detection in off-nadir VHR satellite imagery acquired over a dense urban area. The improved LoS-DSM algorithm was executed over a test dataset. The achieved image-elevation co-registration was very successful based on a visual assessment. Then, the generated and co-registered elevation data were applied in elevation-based building detection. The achieved building map was enhanced based on vegetation and occlusion masks as well as some morphological operations. The quality of the detection was evaluated based on manually generated reference data. The overall detection quality was found to be more than 90% with almost 95% of complete and correct detection. This level of performance in such a challenging dense urban area proves the high success of the disparity-based image-data co-registration as well as the applicability of the developed LoS-DSM elevations to detecting building objects even in off-nadir VHR satellite images acquired over dense urban areas.
关键词： off-nadir VHR images,urban areas,building detection,LoS-DSM,image-elevation co-registration
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automated Building Energy Consumption Estimation from Aerial Imagery
摘要： This paper presents a methodology for automatically estimating the energy consumption of buildings from aerial imagery using data from Gainesville, Florida. By detecting buildings in the imagery using convolutional neural networks and extracting features from those building annotations, we use only imagery-derived features to estimate building energy consumption using random forests regression. For individual buildings, we achieve a predictive R2 value of 0.26, and with spatial aggregation over an area of 400m×400m our predictive R2 value increases to 0.95. We also explore the sensitivity of these estimates to errors in the building estimation process. Our results indicate that information limited to the size and shape of buildings, provides substantial predictive potential for the energy consumption of buildings.
关键词： energy consumption,machine learning,building detection,aerial imagery
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Integrating Mser into a Fast ICA Approach for Improving Building Detection Accuracy
摘要： In this paper, a novel technique is presented to detect buildings from very high resolution satellite image. This work builds on the learning of ICA based building detection technique from the very high resolution (VHR) multispectral satellite images presented in [1]. The candidate building pixels obtained through ICA are used to extract maximally stable extremal regions (MSER) which are then filtered using geometric properties to obtain final potential buildings. The technique is aimed at reducing false detection at pixel-level and improving object-level performance of [1]. Combining the two works offers an unsupervised building detection technique which is robust towards size, shape, color, types of rooftops and shadows. A wider test image set consisting of 15 images of different dimensions are used to evaluate performance of the complete detection process. The combined technique achieves object-level precision and recall of 80.64% and 83.65% respectively.
关键词： VHR image,independent component analysis,Maximally stable extremal regions,Building detection
更新于2025-09-09 09:28:46