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

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Showing 476–500 of 518 papers

TitleStatusHype
Generative Adversarial Training for Weakly Supervised Cloud Matting—0
Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics—0
Optimal Dynamic Multi-Resource Management in Earth Observation Oriented Space Information Networks—0
Satellite-Net: Automatic Extraction of Land Cover Indicators from Satellite Imagery by Deep Learning—0
AI-based evaluation of the SDGs: The case of crop detection with earth observation data—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images—0
Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation—0
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++Code0
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones—0
Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images—0
First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems—0
Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing ImageryCode0
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural NetworksCode0
Multitask Learning for Large-scale Semantic Change Detection—0
Fully Convolutional Siamese Networks for Change DetectionCode0
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn—0
MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping—0
Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning—0
Automatic image annotation : the case of deforestation—0
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery—0
M3Fusion: A Deep Learning Architecture for Multi-Scale/Modal/Temporal satellite data fusionCode0
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders—0
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring—0
Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation—0
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