Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks

Huang, Ziyue; Liu, Qingjie; Zhou, Huanyu; Gao, Guangshuai; Xu, Tao; Qi, Wen; Wang, Yunhong

doi:10.1109/jstars.2023.3261866

Cited by 4 publications

(1 citation statement)

References 84 publications

(143 reference statements)

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“…(Corresponding author: Jinha Jung.) methods have dominated recent literature [13]- [15]. Yet, deep model performance can vary based on training conditions and target areas [16], [17].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR Data

Song,

Jung

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study introduces an automated, open-source workflow for large-scale 2D and 3D building mapping using airborne LiDAR data. Uniquely, our workflow operates entirely unsupervised, eliminating the need for any training procedures. We have integrated a specially tailored digital terrain model generation algorithm into our workflow to prevent errors in complex urban landscapes, especially around highways and overpasses. Through fine rasterization of LiDAR point clouds, we've enhanced building-tree differentiation. Additionally, we've reduced errors near water bodies and augmented computational efficiency by introducing a new planarity calculation. Our workflow offers a practical and scalable solution for the mass production of rasterized 2D and 3D building maps from raw airborne LiDAR data. Our method's robustness has been rigorously validated across a diverse dataset in comparison with deep learningbased and hand-digitized products. Through these extensive comparisons, we provide a valuable analysis of building maps generated via different methodologies. We anticipate that our highly scalable building mapping workflow will facilitate the production of reliable 2D and 3D building maps, fostering advances in large-scale urban analysis. The source code for our workflow is publicly accessible at: https://github.com/hunsoosong/airbornelidar-building-mapping.

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“…(Corresponding author: Jinha Jung.) methods have dominated recent literature [13]- [15]. Yet, deep model performance can vary based on training conditions and target areas [16], [17].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR Data

Song,

Jung

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multimodal Co-Learning for Building Change Detection: A Domain Adaptation Framework Using VHR Images and Digital Surface Models

Xie,

Yuan,

Zhu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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In this article, we propose a multimodal co-learning framework for building change detection. This framework can be adopted to jointly train a Siamese bitemporal image network and a height difference map (HDiff) network with labeled source data and unlabeled target data pairs. Three co-learning combinations (vanilla co-learning, fusion co-learning, and detached fusion colearning) are proposed and investigated with two types of colearning loss functions within our framework. Our experimental results demonstrate that the proposed methods are able to take advantage of unlabeled target data pairs and therefore enhance the performance of single-modal neural networks on the target data. In addition, our synthetic-to-real experiments demonstrate that the recently published synthetic dataset SMARS is feasible to be used in real change detection scenarios, where the optimal result is with the F1 score of 79.29%.

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Enhancing Stock Price Prediction with Deep Cross-Modal Information Fusion Network

Mandal,

Kler,

Tiwari

et al. 2024

Fluct. Noise Lett.

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Stock price prediction is considered a classic and challenging task, with the potential to aid traders in making more profitable trading decisions. Significant improvements in stock price prediction methods based on deep learning have been observed in recent years. However, most existing methods are reliant solely on historical stock price data for predictions, resulting in the inability to capture market dynamics beyond price indicators, thus limiting their performance to some extent. Therefore, combining social media text with historical stock price information has proposed a novel stock price prediction method, known as the Deep Cross-Modal Information Fusion Network (DCIFNet). The process is initiated by DCIFNet, which employs temporal convolution processes to encode stock prices and Twitter content. This ensures that each element has sufficient information about its surrounding components. Following this, the outcomes are inputted into a cross-modal fusion structure based on transformers to enhance the integration of crucial information from stock prices and Twitter content. Lastly, a multi-graph convolution attention network is introduced to depict the relationships between different stocks from diverse perspectives. This facilitates the more effective capturing of industry affiliations, Wikipedia references, and associated relationships among linked stocks, ultimately leading to an enhancement in stock price prediction accuracy. Trend prediction and simulated trading experiments are conducted on high-frequency trading datasets spanning nine different industries. Comparative assessments with the Multi-Attention Network for Stock Prediction (MANGSF) method, as well as ablation experiments, confirm the effectiveness of the DCIFNet approach, resulting in an accuracy rate of 0.6309, a marked improvement compared to representative methods in the field.

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Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks

Cited by 4 publications

References 84 publications

An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR Data

An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR Data

Multimodal Co-Learning for Building Change Detection: A Domain Adaptation Framework Using VHR Images and Digital Surface Models

Enhancing Stock Price Prediction with Deep Cross-Modal Information Fusion Network

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