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Remote Sensing Based Building Extraction II

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...

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Bibliographic Details
Main Author: Tian, Jiaojiao
Other Authors: Yan, Qin, Awrangjeb, Mohammad, Kallfelz-Sirmacek, Beril
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Fully Convolutional Neural Networks
Semantic Edge Detection
Global Information Awareness
High-resolution Image
N/a
U-net Network Improvement
Deep Learning
Iterative Training
Dense Matching
Attention Enhancement
Building Photovoltaic
Road Extraction
Instance Segmentation
Interactive Segmentation Network
Urban Scale
End-to-end
Map Vectorization
Building Model
Building Footprint
High-resolution Remote-sensing Image
Research & Information: General / Bicssc
Lidar Data
Remote Sensing Images
Hyperspectral Image
Building Reconstruction
Integer Programming
Airborne Earth Observation
Convolutional Neural Networks
Pyramid Architecture
Airborne Lidar
Building Extraction
Lidar
Synthetic Aperture Radar (sar)
Multi-source Remote Sensing Image
Half-space
Point Clouds
Cross Level Information Fusion
Semantic Segmentation
Geometric Feature
Geography / Bicssc
Roofscape
Convolutional Neural Network
Graph Segmentation
Remote Sensing Building Extraction
Object Primitive
Reconstruction
Ultrahigh Spatial Resolution
Spatial Attention
Self-supervised Learning
Light Detection And Ranging (lidar)
Farmland Range
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (276 p.)
ISBN:9783036570655
books978-3-0365-7065-5
9783036570648

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