This paper investigates the potential and the limitations of such a deep learning approach by focusing on the permutation of four channels and an encoder–decoder based architecture, with particular attention to how morphological traits and brightness of the solar surface affect the neural network predictions. In particular, the idea of using image-to-image translation to virtually produce extreme ultraviolet channels has been proposed in several recent studies, as a way to both enhance missions with fewer available channels and to alleviate the challenges due to the low downlink rate in deep space. The Solar Dynamics Observatory (SDO), a NASA multispectral decade-long mission that has been daily producing terabytes of observational data from the Sun, has been recently used as a use case to demonstrate the potential of machine-learning methodologies and to pave the way for future deep space mission planning. Machine Learning and the Physical Sciences workshop, NeurIPS 2022Įxploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-image Translation Importantly, the model also recovers transits found by volunteers but missed by current automated methods. We find using volunteer scores significantly improves performance over synthetic data, and enables the recovery of known planets at a precision and rate matching that of the volunteers. In this work, we train a 1-D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. However, volunteer-labelled transits recently collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. While current methods for short-period exoplanet detection work effectively due to periodicity in the light curves, there lacks a robust approach for detecting single-transit events. Journal of Photogrammetry and Remote Sensing (Jan 2023)ĭiscovering Long-period Exoplanets using Deep Learning with Citizen Science LabelsĪutomated planetary transit detection has become vital to prioritize candidates for expert analysis given the scale of modern telescopic surveys. Muhammed Razzak, Gonzalo Mateo-Garcia, Gurvan Lecuyer, Luis Gomez-Chova, Yarin Gal, Freddie Kalaitzis Furthermore, we conduct the first assessment of the util. We show that MISR is superior to single-image super-resolution and other baselines on a range of image fidelity metrics. We, additionally, introduce a radiometric consistency module into MISR model the to preserve the high radiometric resolution of the Sentinel-2 sensor. We present the first results of applying multi-image super-resolution (MISR) to multi-spectral remote sensing imagery. To that end, we curate a multi-spectral multi-image super-resolution dataset, using PlanetScope imagery from the SpaceNet 7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same imagery as the low-resolution imagery. ![]() High-resolution imagery is however expensive, while lower resolution imagery is often freely available and can be used by the public for range of social good applications. High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects. ![]() Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation Hofmann, Neil Hutchinson, Camila Javiera, Jeffrey Moersch, Claire Mondro, Nora Nofke, Victor Parro, Connie Rodriguez, Pablo Sobron, Philippe Sarazzin, David Wettergreen, the SETI Institute NAI Team Cabrol, Michael Phillips, Cinthya Tebes-Cayo, Freddie Kalaitzis, Diego Ayma, Cecilia Demergasso, Guillermo Chong-Diaz, Kevin Lee, Nancy Hinman, Kevin L. Artificial intelligence–machine learning models successfully identified geologic features with high probabilities for containing biosignatures a. Drone flight imagery connected simulated HiRISE data to ground surveys, spectroscopy and biosignature mapping to reveal predictable distributions linked to environmental factors. We advance an adaptable framework that couples statistical ecology with deep learning to recognize and predict biosignature patterns at nested spatial scales in a polyextreme terrestrial environment. Understanding whether the distribution of terrestrial biosignatures is characterized by recognizable and predictable patterns could yield signposts to optimize search efforts for life on other terrestrial planets. In the search for biosignatures on Mars, there is an abundance of data from orbiters and rovers to characterize global and regional habitability, but much less information is available at the scales and resolutions of microbial habitats and biosignatures. Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues
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