Google dataset search and DOI for data in the ESA space science archives

Masson, A.; Marchi, Guido De; Merın, Bruno; Sarmiento, M. H.; Wenzel, David L.; Martínez, Beatriz Cantelar

doi:10.1016/j.asr.2021.01.035

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…ESA will soon launch a new "Swarm Data Handbook" as a tool for discovery and navigation of the Swarm products and their science level metadata (i.e., explanations of the data and links to other resources), giving a more convenient interface than the current documentation. Persistent identifiers (i.e., Digital Object Identifiers, DOI) and JSON-LD (JavaScript Object Notation for Linked Data) will be attached to each item, which will help these products to follow the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (as explained by, for example, Masson et al, 2021). This is also an important step toward strengthening the ontology of objects within VirES (by including these references) and potentially integrating the products within systems such as SPASE.…”

Section: Future Workmentioning

confidence: 99%

Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem

Smith

Pačes²

2022

Front. Astron. Space Sci.

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ESA’s Swarm mission is a constellation probing both Earth’s interior and geospace, delivering magnetic and plasma measurements which are used to generate many derived data products. From empirical magnetic field models of the core, crust, ionosphere, and magnetosphere, to multi-point estimates of ionospheric currents and in-situ plasma properties, these are challenging to navigate, process, and visualize. The VirES for Swarm platform (https://vires.services) has been built to tackle this problem, providing tools to increase usability of Swarm data products. The VirES (Virtual environments for Earth Scientists) platform provides both a graphical web interface and an API to access and visualise Swarm data and models. This is extended with a cloud-hosted development environment powered by JupyterHub (the “Virtual Research Environment/VRE”). VirES provides two API’s: the full VirES API for which a dedicated Python client is provided, viresclient, and the more interoperable Heliophysics API (HAPI). The VRE is furnished with a bespoke Python environment containing thematic libraries supporting science with Swarm. This service aims to ease the pathway for scientists writing computer code to analyze Swarm data products, increase opportunities for collaboration, and leverage cloud technologies. Beyond simply providing data and model access to Python users, it is extremely helpful to provide higher-level analysis and visualization tools, and ready-to-use code recipes that people can explore and extend. Critically for space physics, this involves crossover with many other datasets and so it is highly valuable to embed such tools within the wider data and software ecosystems. Through Swarm DISC (Data, Innovation, and Science Cluster), we are tackling this through cookbooks and Python libraries. Cookbooks are built and presented using Jupyter technologies, and tested to work within the VRE. A new library we are building is SwarmPAL, which includes tools for time-frequency analysis and inversion of magnetic field measurements for electric current systems, among others, while relying on the VirES server to provide data portability and other utilities. This paper reviews the current state of these tools and services for Swarm, particularly in the context of the Python in Heliophysics Community, and the wider heliophysics and geospace data environment.

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Section: Future Workmentioning

confidence: 99%

Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem

Smith

Pačes²

2022

Front. Astron. Space Sci.

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“…As described in Section 2.2, the coarse-scale and intermediate-scale U-nets have been removed in MADNet 2.0, and consequently, the two-stage training process is no longer needed. We have therefore formed a single new training dataset consisting of 4 m/pixel resampled HiRISE ORI and DTM crops, 2 m/pixel resampled HiRISE ORI and DTM crops, and in addition, 36 m/pixel downsampled CTX ORI and DTM crops from the 2300 publicly available iMars (http://www.i-mars.eu/, accessed on 15 October 2021) CTX DTMs and ORIs [8,9] are now included (available at https://www.cosmos.esa.int/web/psa/UCL-MSSL_iMars_CTX_v1.0, accessed on 15 October 2021), all with an identical tile size of 512 × 512 pixels.…”

Section: Network Trainingmentioning

confidence: 99%

“…We propose the new MADNet 2.0 system that employs a simplified and optimised single-image DTM estimation network for pixel-scale DTM retrieval together with a coarse-to-fine reconstruction scheme on top of the DTM inference process. A new set of training data is constructed using selected PDS HiRISE DTMs and iMars CTX DTMs [8] which are available through the ESA's Guest Storage Facility [9] (https://www.cosmos.esa.int/web/psa/ucl-mssl_meta-gsf, accessed on 15 October 2021). We demonstrate the new MADNet 2.0 system with single-view HiRISE images over the ExoMars Rosalind Franklin rover's landing site at Oxia Planum [7,10].…”

Section: Introductionmentioning

confidence: 99%

MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

et al. 2021

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The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25–50 cm/pixel of the surface of Mars. However, due to the spatial resolution being so high, the total area covered by HiRISE targeted stereo acquisitions is very limited. This results in a lack of the availability of high-resolution digital terrain models (DTMs) which are better than 1 m/pixel. Such high-resolution DTMs have always been considered desirable for the international community of planetary scientists to carry out fine-scale geological analysis of the Martian surface. Recently, new deep learning-based techniques that are able to retrieve DTMs from single optical orbital imagery have been developed and applied to single HiRISE observational data. In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes. In particular, we employ optimised single-scale inference and multi-scale reconstruction (in MADNet 2.0), instead of multi-scale inference and single-scale reconstruction (in MADNet 1.0), to produce more accurate large-scale topographic retrieval with boosted fine-scale resolution. We demonstrate the improvements of the MADNet 2.0 DTMs produced using HiRISE images, in comparison to the MADNet 1.0 DTMs and the published Planetary Data System (PDS) DTMs over the ExoMars Rosalind Franklin rover’s landing site at Oxia Planum. Qualitative and quantitative assessments suggest the proposed MADNet 2.0 system is capable of producing pixel-scale DTM retrieval at the same spatial resolution (25 cm/pixel) of the input HiRISE images.

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“…The 20 m/pixel CE-2 DTM is refined using MADNet to a higher resolution at 14 m/pixel (twice the pixel resolution of the corresponding 7 m ORI mosaic) and is used to fill in the gaps of the LROC NAC MADNet DTM mosaic. The final LROC NAC MADNet DTM mosaics (with and without CE-2 for gap filling), alongside a separate LROC NAC ORI 50 cm mosaic created at JPL (Jet Propulsion Laboratory), are all made publicly available through the ESA Guest Storage Facility (GSF) [34] at https://doi.org/10.57780/esa-fb921t3 (accessed on 1 March 2023).…”

Section: Introductionmentioning

confidence: 99%

Large Area High-Resolution 3D Mapping of the Von Kármán Crater: Landing Site for the Chang’E-4 Lander and Yutu-2 Rover

et al. 2023

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We demonstrate the creation of a large area of high-resolution (260 × 209 km2 at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang’E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang’E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive’s guest storage facility.

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Google dataset search and DOI for data in the ESA space science archives

Cited by 13 publications

References 16 publications

Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem

Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem

MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

Large Area High-Resolution 3D Mapping of the Von Kármán Crater: Landing Site for the Chang’E-4 Lander and Yutu-2 Rover

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