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Morphological Entities detection and characterisation from 3-D laser scanned point-clouds

Project description

Focusing on landscape reshaping processes’ data

Landscape reshaping processes exert great impact on wildlife, humans and their habitats. Over the last decades, the use of high-resolution laser scanning technologies to document, monitor, and analyse the morphological signatures these processes imprint on the terrain has improved our understanding of their nature and helped develop strategies to avert hazards and deliver a more sustainable future. However, the fragmented nature of current research in this area prevents researchers from taking full advantage of the large amounts of available data. The EU-funded MorE3D project will introduce a processing framework that will combine the data with geoscientist practices to improve the interaction with the data by highlighting features and offering analyses necessary for the assessment of natural processes.

Objective

Landscape reshaping processes have an immense effect on humans, being a fundamental component of their habitat. The morphological signatures they leave on the terrain enable us to trace them, understand their nature, develop strategies to avert hazards and provide a more sustainable future. As such entities are better traced in their natural 3-D shape, the last decade has seen an expedite growth in the use of high-resolution laser scanning (LiDAR) technologies to document, monitor, and analyse them. Nonetheless, the unorganised nature, span and massive data volume, turn the interaction with the acquired data cumbersome and difficult. Hence, common practices are rooted in manual feature delineation or in use of off-the-shelf raster-based tools, which were developed for different applications and scales. The outcome may be subjective and prone to misidentifications or distortions. As there is an evident gap between the richness of the data and geoscientists practices, I propose in MorE3D to develop a new processing framework that strengthens that link. This will enable to highlight features, provide quantitative morphometric information, and facilitate analysis of trends and patterns which are essential to asses natural processes. Based on the understanding of the geometric signature recorded within the data, I propose to cast this as an energy-based approach that uses global optimisation to detect entities. Furthermore, as many applications combine active and passive sensors (lasers and cameras, for example) or use drone-based imaging to supplement the data during acquisition, I will to further extend the proposed scheme and develop a unified multichannel optimisation framework, where all acquired information is integrated and utilised. Such models will open new avenues to analyse features, detect patterns, trace changes, and essentially enable accurate measurements of the processes affecting landforms, while paving the way to the relevant research communities.

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Topic(s)

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Funding Scheme

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

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(opens in new window) H2020-MSCA-IF-2019

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Coordinator

TECHNISCHE UNIVERSITAET WIEN
Net EU contribution

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.

€ 174 167,04
Address
KARLSPLATZ 13
1040 Wien
Austria

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Region
Ostösterreich Wien Wien
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 174 167,04
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