SOTAVerified

Earth Observation

Earth Observation (EO) refers to the use of remote sensing technologies to monitor land, marine (seas, rivers, lakes) and atmosphere. Satellite-based EO relies on the use of satellite-mounted payloads to gather imaging data about the Earth’s characteristics. The images are then processed and analyzed in order to extract different types of information that can serve a very wide range of applications and industries.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–350 of 518 papers

TitleStatusHype
QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering—0
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation dataCode0
A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data—0
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar dataCode0
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation—0
MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images—0
Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean—0
SeasFire as a Multivariate Earth System Datacube for Wildfire DynamicsCode0
Better, Not Just More: Data-Centric Machine Learning for Earth Observation—0
Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms—0
Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSICode0
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data—0
Challenges in data-based geospatial modeling for environmental research and practice—0
Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing—0
Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection—0
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
Standardized Analysis Ready (STAR) data cube for high-resolution Flood mapping using Sentinel-1 data—0
Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer LearningCode0
Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning—0
There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation—0
Foundation Models for Generalist Geospatial Artificial IntelligenceCode0
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery—0
Fewshot learning on global multimodal embeddings for earth observation tasks—0
SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion ModelsCode0
Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection—0
SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images—0
MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images—0
An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 SatelliteCode0
Multi-Task Hypergraphs for Semi-supervised Learning using Earth ObservationsCode0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric SpaceCode0
Poverty rate prediction using multi-modal survey and earth observation data—0
Understanding the impacts of crop diversification in the context of climate change: a machine learning approach—0
SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution—0
General-Purpose Multimodal Transformer meets Remote Sensing Semantic SegmentationCode0
Sparse Graphical Linear Dynamical Systems—0
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery—0
On-orbit model training for satellite imagery with label proportionsCode0
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification—0
Context-Aware Change Detection With Semi-Supervised Learning—0
Reducing Uncertainties of a Chained Hydrologic-hydraulic Model to Improve Flood Forecasting Using Multi-source Earth Observation Data—0
Over-the-Air Federated Learning in Satellite systems—0
Improve State-Level Wheat Yield Forecasts in Kazakhstan on GEOGLAM's EO Data by Leveraging A Simple Spatial-Aware Technique—0
Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models—0
On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites—0
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward—0
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation—0
Show:102550
← PrevPage 7 of 11Next →

No leaderboard results yet.