VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery
Authors
Keywords
-
Journal
ISPRS International Journal of Geo-Information
Volume 12, Issue 11, Pages 454
Publisher
MDPI AG
Online
2023-11-06
DOI
10.3390/ijgi12110454
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improvement of deep learning Method for water body segmentation of remote sensing images based on attention modules
- (2023) Tiantian Shi et al. Earth Science Informatics
- Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation
- (2023) Min-Gyu Lee et al. Forests
- RSLC-Deeplab: A Ground Object Classification Method for High-Resolution Remote Sensing Images
- (2023) Zhimin Yu et al. Electronics
- VEPL Dataset: A Vegetation Encroachment in Power Line Corridors Dataset for Semantic Segmentation of Drone Aerial Orthomosaics
- (2023) Mateo Cano-Solis et al. Data
- Automatic Extraction of Power Lines from Aerial Images of Unmanned Aerial Vehicles
- (2022) Jiang Song et al. SENSORS
- ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
- (2020) Foivos I. Diakogiannis et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A review of deep learning methods for semantic segmentation of remote sensing imagery
- (2020) Xiaohui Yuan et al. EXPERT SYSTEMS WITH APPLICATIONS
- MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery
- (2020) Qing Zhu et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture
- (2019) Petra Bosilj et al. Journal of Field Robotics
- Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks
- (2019) Michael Wurm et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
- (2019) Daifeng Peng et al. Remote Sensing
- Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net
- (2019) Xiaofei Yang et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- A survey on deep learning techniques for image and video semantic segmentation
- (2018) Alberto Garcia-Garcia et al. APPLIED SOFT COMPUTING
- Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States
- (2018) Hsiuhan Lexie Yang et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- (2018) Liang-Chieh Chen et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
- (2018) Van Nhan Nguyen et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Vegetation encroachment monitoring for transmission lines right-of-ways: A survey
- (2012) Junaid Ahmad et al. ELECTRIC POWER SYSTEMS RESEARCH
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started