Mengjia Qiao
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View article: Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference
Entity class-restricted twin attention-based aggregation for geographic knowledge graph inference Open
Geographic knowledge graphs (GeoKGs) describe and simulate geographic phenomena by storing geographic entities with their semantic relations, providing a foundation for many real-world tasks such as urban function identification. However, …
View article: A Multi-Scale attention network for building extraction from high-resolution remote sensing images
A Multi-Scale attention network for building extraction from high-resolution remote sensing images Open
The information in remote sensing images often leads to incomplete building contours and suboptimal adaptability to complex building scenes. To address these issues, we propose a novel multi-scale network with dual attention mechanisms to …
View article: NPP estimation by fusing geodetector and deep spatio-temporal networks
NPP estimation by fusing geodetector and deep spatio-temporal networks Open
In recent years, deep learning has demonstrated significant potential in net primary productivity (NPP) estimation but the existing methods fall short in fully exploiting the spatio-temporal dependencies inherent in remote sensing data for…
View article: Winter wheat mapping using unbalanced multi-source remote sensing data
Winter wheat mapping using unbalanced multi-source remote sensing data Open
The integration of optical and synthetic aperture radar (SAR) remote sensing images enhances the acquisition of winter wheat planting information and improves the accuracy of winter wheat mapping. However, the distinct imaging mechanisms o…
View article: DffViT: Dual-Branch Feature Fusion Vision Transformer for Remote Sensing Image Retrieval
DffViT: Dual-Branch Feature Fusion Vision Transformer for Remote Sensing Image Retrieval Open
Remote sensing image retrieval (RSIR) is crucial for applications, such as land cover mapping, urban monitoring, disaster response, and environmental change analysis. However, the high intraclass diversity and interclass similarity in remo…
View article: Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images
Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images Open
The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered su…
View article: Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images
Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images Open
Classification tasks on land cover (LC) mapping are challenging due to the complex and heterogeneous characteristics of remote sensing images(RSIs). Current LC classifications are mainly based on deep convolutional neural networks (DCNNs),…
View article: Exploring multiple crowdsourced data to learn deep convolutional neural networks for road extraction
Exploring multiple crowdsourced data to learn deep convolutional neural networks for road extraction Open
Road extraction from high-resolution remote sensing images (HRSIs) is essential for applications in various areas. Although deep convolutional neural networks (DCNNs) have exhibited remarkable success in road extraction, the performance re…
View article: LR‐RoadNet: A long‐range context‐aware neural network for road extraction via high‐resolution remote sensing images
LR‐RoadNet: A long‐range context‐aware neural network for road extraction via high‐resolution remote sensing images Open
Road extraction from high‐resolution remote sensing images (HRSIs) has great importance in various practical applications. However, most existing road extraction methods have considerable limitation in capturing long‐range shape feature of…
View article: Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks
Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks Open
Crop yield prediction has played a vital role in maintaining food security and has been extensively investigated in recent decades. Most research has focused on excavating fixed spectral information from remote sensing images. However, the…
View article: Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process
Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process Open
Accurate and timely prediction of crop yield based on remote sensing data is important for food security. However, crop growth is a complex process, which makes it quite difficult to achieve better performance. To address this problem, a n…
View article: Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset
Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset Open
In recent years, convolutional neural networks (CNNs) have made great achievements in object extraction from very high-resolution (VHR) images. However, most existing approaches require large quantities of clean and accurate training data …