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Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-snapshot

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-030-03398-9_30openalex-snapshot

Large-Scale Structure from Motion with Semantic Constraints of Aerial Images

inferredcites →https://doi.org/10.1007/978-3-030-58452-8_13openalex-snapshot

End-to-End Object Detection with Transformers

inferredcites →https://doi.org/10.1007/978-3-030-58555-6_12openalex-snapshot

Disentangled Non-local Neural Networks

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/s11263-021-01515-2openalex-snapshot

BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation

inferredcites →https://doi.org/10.1016/j.compag.2020.105812openalex-snapshot

Identifying sunflower lodging based on image fusion and deep semantic segmentation with UAV remote sensing imaging

inferredcites →https://doi.org/10.1016/j.eswa.2021.116210openalex-snapshot

Deep-learning-based solution for data deficient satellite image segmentation

inferredcites →https://doi.org/10.1016/j.ijmedinf.2022.104831openalex-snapshot

Semantic segmentation of bone structures in chest X-rays including unhealthy radiographs: A robust and accurate approach

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.05.009openalex-snapshot

UAVid: A semantic segmentation dataset for UAV imagery

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.07.001openalex-snapshot

Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.09.025openalex-snapshot

Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning

inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.06.006openalex-snapshot

Real-time Semantic Segmentation with Context Aggregation Network

inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.09.005openalex-snapshot

ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery

inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.05.004openalex-snapshot

Density Map-based vehicle counting in remote sensing images with limited resolution

inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.06.008openalex-snapshot

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery

inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.08.024openalex-snapshot

A hybrid image segmentation method for building extraction from high-resolution RGB images

inferredcites →https://doi.org/10.1016/j.jag.2020.102117openalex-snapshot

Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles

inferredcites →https://doi.org/10.1016/j.jag.2021.102456openalex-snapshot

A review on deep learning in UAV remote sensing

inferredcites →https://doi.org/10.1016/j.jag.2022.102953openalex-snapshot

LEARD-Net: Semantic segmentation for large-scale point cloud scene

inferredcites →https://doi.org/10.1016/j.measurement.2021.110478openalex-snapshot

Segmentation of unhealthy leaves in cruciferous crops for early disease detection using vegetative indices and Otsu thresholding of aerial images

inferredcites →https://doi.org/10.1016/j.patcog.2022.109019openalex-snapshot

UAVformer: A Composite Transformer Network for Urban Scene Segmentation of UAV Images

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2017.189openalex-snapshot

Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

inferredcites →https://doi.org/10.1109/cvpr.2019.00326openalex-snapshot

Dual Attention Network for Scene Segmentation

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00681openalex-snapshot

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01177openalex-snapshot

TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot

Learning Deconvolution Network for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv48922.2021.00009openalex-snapshot

CvT: Introducing Convolutions to Vision Transformers

inferredcites →https://doi.org/10.1109/iccv48922.2021.00041openalex-snapshot

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

inferredcites →https://doi.org/10.1109/iccv48922.2021.00060openalex-snapshot

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

inferredcites →https://doi.org/10.1109/iccv48922.2021.00061openalex-snapshot

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

inferredcites →https://doi.org/10.1109/iccv48922.2021.00062openalex-snapshot

Incorporating Convolution Designs into Visual Transformers

inferredcites →https://doi.org/10.1109/iccv48922.2021.00359openalex-snapshot

Rethinking Transformer-based Set Prediction for Object Detection

inferredcites →https://doi.org/10.1109/iccv48922.2021.00710openalex-snapshot

ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv48922.2021.00717openalex-snapshot

Segmenter: Transformer for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv48922.2021.01196openalex-snapshot

Vision Transformers for Dense Prediction

inferredcites →https://doi.org/10.1109/iccvw.2019.00113openalex-snapshot

ShelfNet for Fast Semantic Segmentation

inferredcites →https://doi.org/10.1109/jstars.2021.3069909openalex-snapshot

UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information

inferredcites →https://doi.org/10.1109/jstars.2021.3104382openalex-snapshot

Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images

inferredcites →https://doi.org/10.1109/tits.2017.2750080openalex-snapshot

ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation

inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

inferredcites →https://doi.org/10.1145/2856058openalex-snapshot

Multitask Low-Rank Affinity Graph for Image Segmentation and Image Annotation

inferredcites →https://doi.org/10.3390/app112210713openalex-snapshot

Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

inferredcites →https://doi.org/10.3390/app12062877openalex-snapshot

Joint Semantic Understanding with a Multilevel Branch for Driving Perception

inferredcites →https://doi.org/10.3390/rs12020221openalex-snapshot

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

inferredcites →https://doi.org/10.3390/rs12152490openalex-snapshot

Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks

inferredcites →https://doi.org/10.3390/rs13163065openalex-snapshot

Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

inferredcites →https://doi.org/10.3390/rs14030533openalex-snapshot

Semantic Segmentation of Very-High-Resolution Remote Sensing Images via Deep Multi-Feature Learning

inferredcites →https://doi.org/10.3390/s19071486openalex-snapshot

Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data

inferredcites →https://doi.org/10.3390/s20020563openalex-snapshot

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

inferredcites →https://doi.org/10.48550/arxiv.1811.11721openalex-snapshot

CCNet: Criss-Cross Attention for Semantic Segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s12145-024-01355-xopenalex-snapshot

Semantic segmentation of oblique UAV video based on ConvLSTM in complex urban area

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122120openalex-snapshot

Robust unsupervised domain adaptation by retaining confident entropy via edge concatenation

inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2024.03.012openalex-snapshot

Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review

inferred← citeshttps://doi.org/10.1016/j.knosys.2026.116122openalex-snapshot

Deep learning methods for disease detection in cotton fields through UAV imagery: A review

inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101465openalex-snapshot

Road extraction in diverse urban environments using UAV data and nDSM perturbations: A case of Bhopal, India

inferred← citeshttps://doi.org/10.1038/s41598-025-07908-4openalex-snapshot

Lightweight multiscale information aggregation network for land cover land use semantic segmentation from remote sensing images

inferred← citeshttps://doi.org/10.1080/01431161.2025.2518504openalex-snapshot

Classification of informal settlements using novel object-based machine learning algorithms and drone imagery, Slovo Park, Johannesburg, South Africa

inferred← citeshttps://doi.org/10.1088/1361-6501/addf66openalex-snapshot

Integrating advanced remote sensing technologies and machine learning in urban forestry: a comprehensive review and future outlook

inferred← citeshttps://doi.org/10.1109/iciibms60103.2023.10347663openalex-snapshot

Semantic segmentation of unmanned aerial vehicle image based on Resnet-Unet

inferred← citeshttps://doi.org/10.1109/jstars.2025.3643918openalex-snapshot

UESDNet: UAV Visual Multitask Perception Via Expert-Guided Prior With Mutual Optimization of Segmentation and Detection

inferred← citeshttps://doi.org/10.1145/3801438.3802744openalex-snapshot

Research on a Multi-Dimensional Financial Signal Anomaly Detection Model Incorporating Transformers

inferred← citeshttps://doi.org/10.1145/3828658openalex-snapshot

Recent Advances in Transformer and Large Language Models for UAV Applications

inferred← citeshttps://doi.org/10.15587/1729-4061.2023.277427openalex-snapshot

Improving a method for segmenting optical-electronic images acquired from space observation systems based on the firefly algorithm

inferred← citeshttps://doi.org/10.20998/2522-9052.2024.2.06openalex-snapshot

COMPARATIVE ANALYSIS OF SPECTRAL ANOMALIES DETECTION METHODS ON IMAGES FROM ON-BOARD REMOTE SENSING SYSTEMS

inferred← citeshttps://doi.org/10.26906/sunz.2024.2.005openalex-snapshot

МЕТОД ПОШУКУ ОБ’ЄКТІВ ІНТЕРЕСУ ЗА СПЕКТРАЛЬНИМИ ОЗНАКАМИ НА ЗОБРАЖЕННЯХ З АКТИВНОЇ ОПТИКО-ЕЛЕКТРОННОЇ СИСТЕМИ СПОСТЕРЕЖЕННЯ

inferred← citeshttps://doi.org/10.3389/feart.2025.1530234openalex-snapshot

Forecasting formation density from well logging data based on machine learning model

inferred← citeshttps://doi.org/10.3390/jmse14100945openalex-snapshot

MPFT-UNet: A Boundary-Refined and Multi-Scale Dynamic Fusion Network for UAV-Based Port Ship Segmentation

inferred← citeshttps://doi.org/10.3390/math11163611openalex-snapshot

EvolveNet: Evolving Networks by Learning Scale of Depth and Width

inferred← citeshttps://doi.org/10.4018/979-8-3693-9045-0.ch013openalex-snapshot

Semantic Image Segmentation Employing U-Net-Based Ensemble Model

inferred← citeshttps://doi.org/10.5829/ije.2024.37.05b.18openalex-snapshot

Semantic Segmentation of Aerial Imagery: A Novel Approach Leveraging Hierarchical Multi-scale Features and Channel-based Attention for Drone Applications

inferred← citeshttps://doi.org/10.7717/peerj-cs.1451openalex-snapshot

Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system

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