Remote Sensing Based Building Extraction II

• Muut tekijät: Tian, Jiaojiao (Toimittaja), Yan, Qin (Toimittaja), Awrangjeb, Mohammad (Toimittaja), Kallfelz-Sirmacek, Beril (Toimittaja), Demir, Nusret (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Aiheet: Research & information: general; Geography; building extraction; high-resolution remote-sensing image; semantic edge detection; semantic segmentation; building footprint; map vectorization; convolutional neural network; airborne LiDAR; graph segmentation; object primitive; geometric feature; road extraction; high-resolution image; hyperspectral image; synthetic aperture radar (SAR); light detection and ranging (LiDAR); farmland range; attention enhancement; U-Net network improvement; multi-source remote sensing image; building model; reconstruction; half-space; LiDAR data; urban scale; interactive segmentation network; deep learning; iterative training; remote sensing images; spatial attention; global information awareness; cross level information fusion; dense matching; convolutional neural networks; end-to-end; pyramid architecture; building reconstruction; LiDAR; point clouds; integer programming; airborne Earth observation; ultrahigh spatial resolution; instance segmentation; fully convolutional neural networks; roofscape; remote sensing building extraction; building photovoltaic; self-supervised learning; n/a
• Linkit: DOAB: download the publication; DOAB: description of the publication

Building extraction from remote sensing data plays an important role in geospatial applications such as urban planning, disaster management, navigation, and updating geographic databases. The rapid development of image processing techniques and the accessibility of very-high-resolution multispectral...