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Titania: Mapping Forest Tree Dieback with Self-Distilling U-Net on Sentinel-2 Images

https://doi.org/10.2139/ssrn.4779254
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Monitoring ash dieback ( Hymenoscyphus fraxineus ) in British forests using hyperspectral remote sensing
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U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-67389-9_44openalex-extracted
Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks
inferredcites →https://doi.org/10.1007/bfb0026683openalex-extracted
Text categorization with Support Vector Machines: Learning with many relevant features
inferredcites →https://doi.org/10.1007/s10340-022-01490-8openalex-extracted
Patterns, drivers and detectability of infestation symptoms following attacks by the European spruce bark beetle
inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.04.015openalex-extracted
Deep learning in remote sensing applications: A meta-analysis and review
inferredcites →https://doi.org/10.1016/j.jag.2022.102685openalex-extracted
An attention-based U-Net for detecting deforestation within satellite sensor imagery
inferredcites →https://doi.org/10.1016/j.jag.2022.102840openalex-extracted
Forest mapping and monitoring in Africa using Sentinel-2 data and deep learning
inferredcites →https://doi.org/10.1016/j.jnc.2019.125747openalex-extracted
Towards a set of national forest inventory indicators to be used for assessing the conservation status of the habitats directive forest habitat types
inferredcites →https://doi.org/10.1016/j.oneear.2020.05.001openalex-extracted
Applications in Remote Sensing to Forest Ecology and Management
inferredcites →https://doi.org/10.1016/j.ophoto.2022.100024openalex-extracted
Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning
inferredcites →https://doi.org/10.1016/j.rsase.2020.100324openalex-extracted
Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya
inferredcites →https://doi.org/10.1016/j.rse.2020.111702openalex-extracted
A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.164768openalex-extracted
Linking forest ecosystem processes, functions and services under integrative social–ecological research agenda: current knowledge and perspectives
inferredcites →https://doi.org/10.1038/s41598-023-30560-9openalex-extracted
A near real-time web-system for predicting fire spread across the Cerrado biome
inferredcites →https://doi.org/10.1080/19475705.2023.2196370openalex-extracted
Uni-temporal Sentinel-2 imagery for wildfire detection using deep learning semantic segmentation models
inferredcites →https://doi.org/10.1093/forestry/cpad024openalex-extracted
Remote sensing in forestry: current challenges, considerations and directions
inferredcites →https://doi.org/10.1109/jstars.2022.3188201openalex-extracted
Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data
inferredcites →https://doi.org/10.1109/jstars.2023.3312521openalex-extracted
: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images
inferredcites →https://doi.org/10.1109/tmi.2023.3264433openalex-extracted
MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet
inferredcites →https://doi.org/10.1109/tpami.2021.3067100openalex-extracted
Self-Distillation: Towards Efficient and Compact Neural Networks
inferredcites →https://doi.org/10.1111/afe.12536openalex-extracted
Drought and stand susceptibility to attacks by the European spruce bark beetle: A remote sensing approach
inferredcites →https://doi.org/10.1145/3511808.3557528openalex-extracted
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery
inferredcites →https://doi.org/10.3389/fpls.2023.1139232openalex-extracted
Machine learning assisted remote forestry health assessment: a comprehensive state of the art review
inferredcites →https://doi.org/10.3390/electronics11152296openalex-extracted
A Sustainable Deep Learning-Based Framework for Automated Segmentation of COVID-19 Infected Regions: Using U-Net with an Attention Mechanism and Boundary Loss Function
inferredcites →https://doi.org/10.3390/f11090941openalex-extracted
Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon
inferredcites →https://doi.org/10.3390/f13122170openalex-extracted
Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery
inferredcites →https://doi.org/10.3390/rs13020260openalex-extracted
Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning
inferredcites →https://doi.org/10.3390/rs13163314openalex-extracted
Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains
inferredcites →https://doi.org/10.3390/rs13234873openalex-extracted
Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models
inferredcites →https://doi.org/10.3390/rs14236105openalex-extracted
A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping
inferredcites →https://doi.org/10.3390/s22197440openalex-extracted
Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet++
inferredcites →https://doi.org/10.3832/ifor0480-002openalex-extracted
Means of combating forest dieback - EU support for maintaining forest health and vitality
inferredcites →https://doi.org/10.3832/ifor3271-013openalex-extracted
A bark beetle infestation predictive model based on satellite data in the frame of decision support system TANABBO
inferredcites →https://doi.org/10.48550/arxiv.1409.0473openalex-extracted
Neural Machine Translation by Jointly Learning to Align and Translate
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-extracted
Distilling the Knowledge in a Neural Network
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