Astronomical Images Quality Assessment with Automated Machine Learning

Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a large quantity of data, which may then be enhanced with dedicated image editing software after observation sessions. In this study, we show how Image Quality Assessment can be useful for automatically rating astronomical images, and we also develop a dedicated model by using Automated Machine Learning.Comment: 8 pages, accepted at DATA202

Understanding Open Data CSV File Structures for Reuse

International audienceHow to understand the structure of Open Data CSV files in order to be able to reuse them

Open Data Integration - Visualization as an Asset

International audienceFor several years, and even decades, data integration has been a major problem in computer sciences. When it becomes necessary to process information from different data sources, several problems may appear, making the process of integration more difficult. Nowadays, more and more information is being sent and received and is made available on the Web and Data Integration is becoming even more important. This is especially the case in the emerging trend of Open Data (OD). Integrating data from public entities can be a difficult process. Large quantities of datasets are made available. However, an important level of heterogeneity may also exist: Datasets exist in different formats, forms and shapes. While it is important to be able to access this information, it would also be completely useless if we were not able to interpret it. Information Visualization may be an important tool to help the OD integration process. This paper presents problems and barriers which can be encountered in the data integration process, and, more specifically, in the OD integration process. The paper also describes how Information Visualization can be used to facilitate the integration of OD and make the procedure more effective, friendlier, and faster

Information Visualization for CSV Open Data files structure analysis

International audienceNew and different information sources have appeared over the past years (e.g. Blogs, Media, Open Data, Scientific Data and Social Networks). The variety of these sources is growing and the related data volume does not cease to increase exponentially. Open Data (OD) initiatives and platforms are one of the current major data producers, also because the topic seems to be important for many governments world-wide. Given the many fields and sectors involved, OD brings high business and societal potential. The amount and diversity of available information is high. However, analysing and understanding OD in order to exploit is far from being an easy task. Several problems and constraints must be solved. Information Visualization (InfoVis) can help to give a graphical idea of the processed files structure. Given that OD is provided very often as tabular data, this paper focuses on OD CSV files. It presents an overview on the analysis of tabular information. Finally, the paper describes the role of Information Visualization and the way it may help the end-user to understand quickly the structure and issues of OD CSV files

MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope

Modern automated telescopes allow to capture astronomical images in a reproducible way. During the MILAN research project (MachIne Learning for AstroNomy), we have observed deep sky with a Stellina observation station for twelve months from the Luxembourg Greater Region. Thus, we have captured raw images of more than 188 deep sky objects visible from the Northern Hemisphere (galaxies, stars clusters, nebulae, etc.), We have compiled and published this data as the MILAN Sky Survey dataset, allowing interested researchers, industry practitioners and citizens to reuse it

Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: Temperature-based validation and cross-satellite comparison

International audienceHigh spatial resolution land surface temperature (LST, <100 m) is crucial for agricultural water management, crop water stress monitoring, fire mapping, urban heat island study and volcano eruption detection. LST retrievals from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) launched in June 2018, together with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, launched in 1999) and the Landsat series (since 1972), comprise the state-of-the-art high spatial resolution LST datasets publicly accessible. Recently, we generated the ECOSTRESS LST product over Europe and Africa using both the temperature and emissivity separation (TES) and split-window (SW) algorithms under the European ECOSTRESS Hub (EEH). Here, we validated the official Jet Propulsion Laboratory (JPL) TES (Collection 1), EEH TES and EEH SW ECOSTRESS LST products over Europe and Africa between August 1, 2018 and December 31, 2021 by comparing against the in-situ measurements at 9 sites over a wide variety of land cover types. Meanwhile, the validation results were compared with those obtained for ASTER and Landsat LST at the same sites for a thorough understanding of the consistency among these high spatial resolution LST products. The results reveal that the three ECOSTRESS LST products have consistent performances, with an overall RMSE around 2 K. A cold bias around 1 K exists for all three ECOSTRESS LST, which is presumably originated from the radiometric calibration of the sensor in Collection 1 data. The Landsat LST shows a similar accuracy, with an RMSE of 2.20 K and bias of 0.54 K. The EEHSW LST show the highest consistency with Landsat LST, possibly due to the identical emissivity correction process. The performance of ASTER LST is also similar, with an RMSE of 1.98 K and bias of 0.9 K. The precisions of all the LST products are around 1.5 K. Future recalibration of the ECOSTRESS Level 1 radiance data in Collection 2 is expected to further improve the accuracy of ECOSTRESS LST. Overall, this study supports the adaptation of LST retrieval algorithms for the future thermal missions

Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: Temperature-based validation and cross-satellite comparison

High spatial resolution land surface temperature (LST, <100 m) is crucial for agricultural water management, crop water stress monitoring, fire mapping, urban heat island study and volcano eruption detection. LST retrievals from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) launched in June 2018, together with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, launched in 1999) and the Landsat series (since 1972), comprise the state-of-the-art high spatial resolution LST datasets publicly accessible. Recently, we generated the ECOSTRESS LST product over Europe and Africa using both the temperature and emissivity separation (TES) and split-window (SW) algorithms under the European ECOSTRESS Hub (EEH). Here, we validated the official Jet Propulsion Laboratory (JPL) TES (Collection 1), EEH TES and EEH SW ECOSTRESS LST products over Europe and Africa between August 1, 2018 and December 31, 2021 by comparing against the in-situ measurements at 9 sites over a wide variety of land cover types. Meanwhile, the validation results were compared with those obtained for ASTER and Landsat LST at the same sites for a thorough understanding of the consistency among these high spatial resolution LST products. The results reveal that the three ECOSTRESS LST products have consistent performances, with an overall RMSE around 2 K. A cold bias around 1 K exists for all three ECOSTRESS LST, which is presumably originated from the radiometric calibration of the sensor in Collection 1 data. The Landsat LST shows a similar accuracy, with an RMSE of 2.20 K and bias of 0.54 K. The EEHSW LST show the highest consistency with Landsat LST, possibly due to the identical emissivity correction process. The performance of ASTER LST is also similar, with an RMSE of 1.98 K and bias of 0.9 K. The precisions of all the LST products are around 1.5 K. Future recalibration of the ECOSTRESS Level 1 radiance data in Collection 2 is expected to further improve the accuracy of ECOSTRESS LST. Overall, this study supports the adaptation of LST retrieval algorithms for the future thermal missions

Evaluating European ECOSTRESS Hub Evapotranspiration Products Retrieved from Three Structurally Contrasting SEB Models over Europe

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio-temporal resolution (~70 m, 1-5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio-temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature retrievals, we generated ECOSTRESS ET products over Europe and Africa using three structurally contrasting models, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non-parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites over 6 different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ~70 W m-2). However, the relatively complex TSEB model produced a higher RMSE of ~90 W m-2. Comparison between STIC ET estimate and the operational ECOSTRESS ET product from NASA PT-JPL model showed a difference in RMSE between the two ET products around 50 W m-2. Substantial overestimation (>80 W m-2) was noted in PT-JPL ET estimates over shrublands and savannas presumably due to the weak constraint of LST in the model. Overall, the EEH is promising to serve as a support to the Land Surface Temperature Monitoring (LSTM) mission