Develop an active learning framework based on the U-Net architecture to efficiently generate training data from PlanetScope imagery, deriving two data sets: (1) burned areas and (2) tree cover. The active learning framework involves the following steps:

Nov 18, 2020 · My idea is to create a machine learning model that can predict if an observation is a real candidate for an exoplanet or not. The data was collected by the Kepler mission that revealed thousands of planets out of our Solar System. Unfortunately, the …

Feb 24, 2024 · This study presents a comprehensive evaluation of various classification algorithms used for the detection of exoplanets using labeled time series data from the Kepler mission.

Nov 17, 2022 · Scientists have developed an open source planetwide dataset of high-resolution Earth observation imagery, thanks to commercial data delivered by ESA’s Third Party Missions (TPM) programme. The product – called WorldStrat – …

Mar 21, 2023 · Given the size of our archive, it’s a veritable playground for Planeteers and our partners to train AI and ML models and to build algorithms that can extract objects and patterns – to find newly-built roads, identify collapsed or raised buildings, monitor change in forests throughout time, or track surveillance balloons over oceans – all ...

Dec 14, 2017 · Using a dataset of more than 15,000 labeled Kepler signals, we created a TensorFlow model to distinguish planets from non-planets. To do this, it had to recognize patterns caused by actual planets, versus patterns caused by other objects like starspots and binary stars.

In this dataset, you can become a space explorer too by analyzing the characteristics of all discovered exoplanets (plus some familiar faces like Mars, Saturn, and even Earth). Data fields include planet and host star attributes, discovery methods, and (of course) date of discovery.

Mar 11, 2023 · Abstract: In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition.

The main research questions addressed by this work are how can artificial intelligence algorithms contribute to the exoplanet detection field, and if it is possible to add technical knowledge through synthetic data to improve the performance of the detector.

this project is to use data generated from Kepler space telescope and come up with machine learning model which could use planetary and stellar features to classify exoplanets into habitable and non-habitable planets.