Project description
Boulder mapping could help reveal how planetary surfaces evolved
Craters are very common surface features on many solid planets and moons. During an impact, rock fragments ejected from the crater cavity could be deposited elsewhere on the surface, where they could potentially form secondary craters. Boulders are the only remnants of these ejected materials. Their size and shape, as well as the terrain on which they are found, provide important insight into the ejection mechanisms. Funded by the Marie Skłodowska-Curie Actions programme, the BOULDERING project plans to use high-resolution imaging and deep learning to further investigate the size and shape distributions of boulder populations. Project results could boost our understanding of the planetary surface evolution.
Objective
Many planetary surfaces are heavily cratered as they witnessed the early stages of Solar System evolution during which impact cratering was a frequent process. Upon impact, rock fragments are ejected from the crater cavity and deposited elsewhere on the surface, where they potentially form secondary craters. The unknown contribution of secondary craters increase crater density and distort crater statistics, which ultimately biases the estimated age of a surface unit, a key diagnostics for understanding the evolution of planetary bodies.
The size and velocity distribution of the ejected rock fragments is a poorly understood aspect so that an important link between crater statistics and planetary surface age keeps missing. One way to close this connection is to make use of the population of boulders (meter-sized rocks) that can be detected on high-resolution images of planetary surfaces, such as the Moon’s. Boulders are the only remnants of the ejected materials and their size and shape as well as the terrain on which they are found provide important insight into the ejection mechanisms. BOULDERING aims to advance the detection of boulders on planetary surfaces from high-resolution imagery using deep learning and to compile size and shape distributions of boulder populations. Based on this, this project will boost our understanding of cratering records and the implications for planetary surface evolution.
A versatile automatic boulder detection algorithm will be developed using a convolutional neural network. This algorithm will first be validated on terrestrial boulder populations in Death Valley and the Mojave Desert and will then be trained with remote sensing data for application on the lunar and martian surfaces. By following this approach, ground data collected on Earth will be used to test the algorithm’s capacity to measure the sizes and shapes of boulders, which is key to make robust inferences on the boulder population on other planetary bodies.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences physical sciences astronomy planetary sciences planetary geology
- engineering and technology environmental engineering remote sensing
- natural sciences physical sciences astronomy planetary sciences natural satellites
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
0313 Oslo
Norway
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.