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Found 8 papers, 2 papers with code
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases.
Fast model inference and training on-board of Satellites
In this work we demonstrate the reliable use of RaVAEn onboard a satellite, achieving an encoding time of 0. 110s for tiles of a 4. 8x4. 8 km$^2$ area.
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
THRawS: A Novel Dataset for Thermal Hotspots Detection in Raw Sentinel-2 Data
Nevertheless, given the growing interest to apply Artificial Intelligence (AI) onboard satellites for time-critical applications, such as natural disaster response, providing raw satellite images could be useful to foster the research on energy-efficient pre-processing algorithms and AI models for onboard-satellite applications.
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data.
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment.
Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
SAR satellites deliver very high resolution observations with a global coverage.
