Selected trends in artificial intelligence for space applications
The development and adoption of artificial intelligence (AI) technologies in space
applications is growing quickly as the consensus increases on the potential benefits …
applications is growing quickly as the consensus increases on the potential benefits …
Deep-Learning-Based System for Change Detection Onboard Earth Observation Small Satellites
C Serief, Y Ghelamallah… - IEEE Journal of Selected …, 2023 - ieeexplore.ieee.org
In recent years, the important evolution in the number, potentiality, and diversity of Earth
observation (EO) satellites has resulted in dramatic increases in the payload data volume …
observation (EO) satellites has resulted in dramatic increases in the payload data volume …
[HTML][HTML] The OPS-SAT case: A data-centric competition for onboard satellite image classification
While novel artificial intelligence and machine learning techniques are evolving and
disrupting established terrestrial technologies at an unprecedented speed, their adaptation …
disrupting established terrestrial technologies at an unprecedented speed, their adaptation …
Fast model inference and training on-board of satellites
Artificial intelligence onboard satellites has the potential to reduce data transmission
requirements, enable real-time decision-making and collaboration within constellations. This …
requirements, enable real-time decision-making and collaboration within constellations. This …
[HTML][HTML] Domain knowledge-driven variational recurrent networks for drought monitoring
M Zhang, MÁ Fernández-Torres… - Remote Sensing of …, 2024 - Elsevier
In the context of climate change, droughts, increasingly frequent and severe, necessitate
effective monitoring. Existing methods, such as drought indices and data-driven models …
effective monitoring. Existing methods, such as drought indices and data-driven models …
[HTML][HTML] Semantic segmentation of methane plumes with hyperspectral machine learning models
Methane is the second most important greenhouse gas contributor to climate change; at the
same time its reduction has been denoted as one of the fastest pathways to preventing …
same time its reduction has been denoted as one of the fastest pathways to preventing …
Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications
Classifying land use and land cover (LULC) is essential for various environmental
monitoring and geospatial analysis applications. This research focuses on land …
monitoring and geospatial analysis applications. This research focuses on land …
Unsupervised wildfire change detection based on contrastive learning
The accurate characterization of the severity of the wildfire event strongly contributes to the
characterization of the fuel conditions in fire-prone areas, and provides valuable information …
characterization of the fuel conditions in fire-prone areas, and provides valuable information …
STARCOP: Semantic Segmentation of Methane Plumes with Hyperspectral Machine Learning Models
Methane is the second most important greenhouse gas contributor to climate change; at the
same time its reduction has been denoted as one of the fastest pathways to preventing …
same time its reduction has been denoted as one of the fastest pathways to preventing …
Onboard Cloud Detection and Atmospheric Correction with Deep Learning Emulators
This paper introduces DTACSNet, a Convolutional Neural Network (CNN) model specifically
developed for efficient onboard atmospheric correction and cloud detection in optical Earth …
developed for efficient onboard atmospheric correction and cloud detection in optical Earth …