64 citations · 72 across the 14 of their papers we have counts for
13 papers · 1 filter
Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping
Yusen Cheng, Lei Fan, Qinfeng Zhu +3
Data-driven methods are widely used in landslide susceptibility mapping (LSM) because they can effectively model the complex relationships between landslides and geo-environmental…
Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling
Qinfeng Zhu, Lei Fan
Panoramic images capture the full visual sphere in a frame, offering context unavailable to conventional cameras. Yet this completeness has an unavoidable geometric cost: the 2-sph…
A generalised pre-training strategy for deep learning networks in semantic segmentation of remotely sensed images
Yuan Fang, Yuanzhi Cai, Jagannath Aryal +4
In the segmentation of remotely sensed images, deep learning models are typically pre-trained using large image databases like ImageNet before fine-tuned on domain-specific dataset…
SO3UFormer: Learning Intrinsic Spherical Features for Rotation-Robust Panoramic Dense Prediction
Qinfeng Zhu, Yunxi Jiang, Lei Fan
Panoramic dense-prediction models, spanning semantic segmentation and depth estimation, are typically trained under a strict gravity-aligned assumption. Real-world captures, howeve…
From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning
Yusen Cheng, Qinfeng Zhu, Lei Fan
Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, and preprocessing-related uncertain…
SwinMamba: A hybrid local-global mamba framework for enhancing semantic segmentation of remotely sensed images
Qinfeng Zhu, Han Li, Liang He +1
Semantic segmentation of remote sensing imagery is a fundamental task in computer vision, supporting a wide range of applications such as land use classification, urban planning, a…
ClassWise-CRF: Category-Specific Fusion for Enhanced Semantic Segmentation of Remote Sensing Imagery
Qinfeng Zhu, Yunxi Jiang, Lei Fan
We propose a result-level category-specific fusion architecture called ClassWise-CRF. This architecture employs a two-stage process: first, it selects expert networks that perform…
MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery
Qinfeng Zhu, Yuan Fang, Lei Fan
Crack detection is a critical task in structural health monitoring, aimed at assessing the structural integrity of bridges, buildings, and roads to prevent potential failures. Visi…
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy
Qinfeng Zhu, Jiaze Cao, Yuanzhi Cai +1
Point cloud semantic segmentation, the process of classifying each point into predefined categories, is essential for 3D scene understanding. While image-based segmentation is wide…
Enhancing Environmental Monitoring through Multispectral Imaging: The WasteMS Dataset for Semantic Segmentation of Lakeside Waste
Qinfeng Zhu, Ningxin Weng, Lei Fan +1
Environmental monitoring of lakeside green areas is crucial for environmental protection. Compared to manual inspections, computer vision technologies offer a more efficient soluti…
Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed Images
Qinfeng Zhu, Yuanzhi Cai, Lei Fan
Recent advancements in autoregressive networks with linear complexity have driven significant research progress, demonstrating exceptional performance in large language models. A r…
Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study
Qinfeng Zhu, Yuan Fang, Yuanzhi Cai +2
Deep learning methods, especially Convolutional Neural Networks (CNN) and Vision Transformer (ViT), are frequently employed to perform semantic segmentation of high-resolution remo…
Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model
Qinfeng Zhu, Yuanzhi Cai, Yuan Fang +4
High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). CNN…