Data fusion of high-resolution satellite images and airborne laser scanning data for building detection in an urban environment

2003 ◽  
Author(s):  
Tao Guo ◽  
Yoshifumi Yasuoka
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Urban Environment ◽  
Laser Scanning ◽  
Satellite Images ◽  
Airborne Laser Scanning ◽  
Building Detection ◽  
Airborne Laser ◽  
High Resolution Satellite Images

An approach to classification of airborne laser scanning point cloud data in an urban environment

2011 ◽  
Vol 32 (24) ◽  
pp. 9151-9169 ◽  
Author(s):  
Cici Alexander ◽  
Kevin Tansey ◽  
Jörg Kaduk ◽  
David Holland ◽  
Nicholas J. Tate
Keyword(s):  
Urban Environment ◽  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Airborne Laser

scholarly journals Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data

2019 ◽  
Vol 11 (4) ◽  
pp. 403 ◽  
Author(s):  
Weijia Li ◽  
Conghui He ◽  
Jiarui Fang ◽  
Juepeng Zheng ◽  
Haohuan Fu ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Building Detection ◽  
Building Extraction ◽  
Three Solutions ◽  
High Resolution Satellite Images ◽  
Building Footprint ◽  
Map Data

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.

scholarly journals A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 3 ◽  
Author(s):  
Muhammad Aamir ◽  
Yi-Fei Pu ◽  
Ziaur Rahman ◽  
Muhammad Tahir ◽  
Hamad Naeem ◽  
...  
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Satellite Images ◽  
Discrete Wavelet ◽  
Building Detection ◽  
Detection Scheme ◽  
Low Contrast ◽  
Line Segments ◽  
High Resolution Satellite Images ◽  
Wide Range

Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.

scholarly journals Airborne laser scanning for high-resolution mapping of Antarctica

Eos ◽  
2005 ◽  
Vol 86 (25) ◽  
pp. 237 ◽  
Author(s):  
Bea Csatho ◽  
Toni Schenk ◽  
William Krabill ◽  
Terry Wilson ◽  
William Lyons ◽  
...  
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
High Resolution Mapping ◽  
Airborne Laser ◽  
Resolution Mapping
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