کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6679595 | 1428033 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automated residential building detection from airborne LiDAR data with deep neural networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Detection of building objects in airborne LiDAR data is an essential task in many types of geospatial data applications such as urban reconstruction and damage assessment. Traditional approaches used in building detection often rely on shape primitives that can be detected by 2D/3D computer vision techniques. These approaches require carefully engineered features which tend to be specific to building types. Furthermore, these approaches are often computationally expensive with the increase of data size. In this paper, we propose a novel approach that employs a deep neural network to recognize and extract residential building objects in airborne LiDAR data. This proposed approach does not require any pre-defined geometric or texture features, and it is applicable to airborne LiDAR data sets with varied point densities and with damaged building objects. The latter makes our approach particularly useful in damage assessment applications. The research results show that the proposed approach is capable of achieving the state-of-the-art accuracy in building detection in these different types of point cloud data sets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advanced Engineering Informatics - Volume 36, April 2018, Pages 229-241
Journal: Advanced Engineering Informatics - Volume 36, April 2018, Pages 229-241
نویسندگان
Zixiang Zhou, Jie Gong,