Remote Sensing Based Building Extraction II

• Other Authors: Tian, Jiaojiao (Editor), Yan, Qin (Editor), Awrangjeb, Mohammad (Editor), Kallfelz-Sirmacek, Beril (Editor), Demir, Nusret (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: 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
• Online Access: 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, hyperspectral, LiDAR, and SAR remote sensing images have further boosted research on building-extraction-related topics. In particular, to meet the recent demand for advanced artificial intelligence models, many research institutes and associations have provided open source datasets and annotated training data, presenting new opportunities to develop advanced approaches for building extraction and monitoring. Hence, there are higher expectations of the efficiency, accuracy, and robustness of building extraction approaches. Additionally, they should meet the demand for processing large city-, national-, and global-scale datasets. Moreover, learning and dealing with imperfect training data remains a challenge, as does unexpected objects in urban scenes such as trees, clouds, and shadows. In addition to building masks, more research has arisen on the automatic generation of LoD2/3 building models from remote sensing data. This follow-up Special Issue of "Remote Sensing-based Building Extraction", has collected more research on cutting-edge approaches to essential urban processes such as 3D reconstruction, automatic building segmentation, and 3D roof modelling.