Journals starting with eart

EarthObserv15 * *Looking from Above: When Earth Observation Meets Vision
* Active learning approach to detecting standing dead trees from ALS point clouds combined with aerial infrared imagery
* Detection of incomplete enclosures of rectangular shape in remotely sensed images
* Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
* Effective semantic pixel labelling with convolutional networks and Conditional Random Fields
* Matching Persistent Scatterers to optical oblique images
* Oil spill candidate detection from SAR imagery using a thresholding-guided stochastic fully-connected conditional random field model
* On the location dependence of convolutional neural network features
* Semantic segmentation of urban scenes by learning local class interactions
* semi-supervised approach for ice-water classification using dual-polarization SAR satellite imagery, A
* Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data
* Universality of wavelet-based non-homogeneous hidden Markov chain model features for hyperspectral signatures
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EarthObv24 * *Computer Vision for Earth Observation Applications
* CNet: A Novel Seabed Coral Reef Image Segmentation Approach Based on Deep Learning
* GAST: Geometry-Aware Structure Transformer
* Modernized Training of U-Net for Aerial Semantic Segmentation
* TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection

EarthVision17 * *EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
* Automatic 3D Reconstruction from Multi-date Satellite Images
* Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs
* Earth Observation Using SAR and Social Media Images
* Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
* Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
* Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning
* Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening
* On the Role of Representations for Reasoning in Large-Scale Urban Scenes
* Robocodes: Towards Generative Street Addresses from Satellite Imagery
* Super-Resolution of Multispectral Multiresolution Images from a Single Sensor
* Temporal Vegetation Modelling Using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-spectral Satellite Images
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EarthVision20 * *EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
* DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
* Deep Regression for Imaging Solar Magnetograms using Pyramid Generative Adversarial Networks
* Density Map Guided Object Detection in Aerial Images
* FGCN: Deep Feature-based Graph Convolutional Network for Semantic Segmentation of Urban 3D Point Clouds
* Meta-Learning for Few-Shot Land Cover Classification
* Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution
* Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
* RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery
* ResDepth: Learned Residual Stereo Reconstruction
* S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis
* Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1
* SpaceNet 6: Multi-Sensor All Weather Mapping Dataset
* StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
* Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
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EarthVision21 * *EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
* Addressing Visual Search in Open and Closed Set Settings
* Combining Remotely Sensed Imagery with Survival Models for Outage Risk Estimation of the Power Grid
* Dot Distance for Tiny Object Detection in Aerial Images
* EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
* Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery
* Learning to predict crop type from heterogeneous sparse labels using meta-learning
* Machine-learned 3D Building Vectorization from Satellite Imagery
* OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World
* Point2color: 3D Point Cloud Colorization Using a Conditional Generative Network and Differentiable Rendering for Airborne LiDAR
* QFabric: Multi-Task Change Detection Dataset
* Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
* Self-supervised multi-image super-resolution for push-frame satellite images
* Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry
* Single View Geocentric Pose in the Wild
* Towards Indirect Top-Down Road Transport Emissions Estimation
* Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner
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EarthVision22 * *EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
* Cross-dataset Learning for Generalizable Land Use Scene Classification
* Fast building segmentation from satellite imagery and few local labels
* Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
* Hephaestus: A large scale multitask dataset towards InSAR understanding
* Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans
* OpenSentinelMap: A Large-Scale Land Use Dataset using OpenStreetMap and Sentinel-2 Imagery
* Prompt-RSVQA: Prompting visual context to a language model for Remote Sensing Visual Question Answering
* Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras
* Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images
* Self-supervised Vision Transformers for Land-cover Segmentation and Classification
* Single-Shot End-to-end Road Graph Extraction
* SpaceNet 8: The Detection of Flooded Roads and Buildings
* Towards assessing agricultural land suitability with causal machine learning
* Transforming Temporal Embeddings to Keypoint Heatmaps for Detection of Tiny Vehicles in Wide Area Motion Imagery (WAMI) Sequences
* Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model
* Unsupervised Change Detection Based on Image Reconstruction Loss
* Urban Building Classification (UBC) - A Dataset for Individual Building Detection and Classification from Satellite Imagery
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EarthVision23 * *EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
* APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP
* Cascaded Zoom-in Detector for High Resolution Aerial Images
* Comprehensive quality assessment of optical satellite imagery using weakly supervised video learning
* Deep unfolding for hyper sharpening using a high-frequency injection module
* DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching
* GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
* Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
* Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping
* L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery
* Masked Vision Transformers for Hyperspectral Image Classification
* Multi-Date Earth Observation NeRF: The Detail Is in the Shadows
* Multi-Modal Multi-Objective Contrastive Learning for Sentinel-1/2 Imagery
* Seasonal Domain Shift in the Global South: Dataset and Deep Features Analysis
* Solar Irradiance Anticipative Transformer
* Sparse Multimodal Vision Transformer for Weakly Supervised Semantic Segmentation
* UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
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