Numerical modeling of wildland surface fire propagation by evolving surface curves

Ambroz, Martin; Balažovjech, Martin; Medl’a, Matej; Mikula, Karol

doi:10.1007/s10444-018-9650-4

Cited by 13 publications

(15 citation statements)

References 27 publications

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“…Then the gradient of the edge detector function is projected to the normal of the curve and the overall curve motion is regularized by using the local curvature of evolving curve [3,4,7,8]. The final mathematical model is given by the corresponding nonlinear intrinsic partial differential equation which is discretized and solved numerically by the flowing finite volume method [9,7,1].…”

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confidence: 99%

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Semi-automatic segmentation of NATURA 2000 habitats in Sentinel-2 satellite images by evolving open curves

Mikula¹,

Urbán²,

Kollár³

et al. 2021

Discrete &Amp; Continuous Dynamical Systems - S

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In this paper we introduce mathematical model and real-time numerical method for segmentation of Natura 2000 habitats in satellite images by evolving open planar curves. These curves in the Lagrangian formulation are driven by a suitable velocity vector field, projected to the curve normal. Besides the vector field, the evolving curve is influenced also by the local curvature representing a smoothing term. The model is numerically solved using the flowing finite volume method discretizing the arising intrinsic partial differential equation with Dirichlet boundary conditions. The time discretization is chosen as an explicit due to the ability of real-time edge tracking. We present the results of semi-automatic segmentation of various areas across Slovakia, from the riparian forests to mountainous areas with scrub pine. The numerical results were compared to habitat boundaries tracked by GPS device in the field by using the mean and maximal Hausdorff distances as criterion.

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confidence: 99%

“…where w = λv N . First, we perform the spatial discretization, which is based on the flowing finite volume method [9,2,1]. Integrating (9) over the finite volume…”

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confidence: 99%

Semi-automatic segmentation of NATURA 2000 habitats in Sentinel-2 satellite images by evolving open curves

Mikula¹,

Urbán²,

Kollár³

et al. 2021

Discrete &Amp; Continuous Dynamical Systems - S

Self Cite

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“…In our propagation model we use our O(n) approach, which makes the overall computations fast and reliable in fire perimeter evolution. Our main idea for the topological changes detection, see also (Balažovjech, et al, 2012), is to create an array of cells over the whole computational domain and to check a narrow strip of cells along the curves. In these cells we subsequently check whether there are two nonneighboring points of one curve, which indicates splitting.…”

Section: Figure 3 -The Example Of a Projection Of The Wind Vector (Whmentioning

confidence: 99%

“…We distinguish two main approaches to handle the curve evolution problems, the so-called Lagrangian (direct or vector-based) approach, see e.g. (Dziuk, 1999), , (Balažovjech, et al, 2012) and the so-called Eulerian (level-set or raster-based) approach, see e.g. (Sethian, 1999), (Osher & Fedkiw, 2002).…”

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confidence: 99%

“…In the Lagrangian approach one evolves directly the curve discretization points, so it is spatially 1-D problem and thus computationally much simpler and faster than the level-set method. However, the Lagrangian approaches need the so-called tangential grid point redistribution (Hou, et al, 1994), (Barrett, et al, 2009), (Mikula & Ševčovič, 2001), and efficient algorithm for the detection and treatment of topological changes during the evolution (Balažovjech, et al, 2012), , (Benninghoff & Garcke, 2014), which are on the other hand automatically handled by the level set method (Sethian, 1999), (Osher & Fedkiw, 2002). When the Lagrangian methods are tangentially stabilized and are able to treat the topological changes fastly, they represent really efficient approaches to 2-D or surface curve evolution…”

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New Lagrangian surface forest fire propagation model

Ambrož¹,

Mikula²

Advances in Forest Fire Research 2018

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A navegação consulta e descarregamento dos títulos inseridos nas Bibliotecas Digitais UC Digitalis, UC Pombalina e UC Impactum, pressupõem a aceitação plena e sem reservas dos Termos e Condições de Uso destas Bibliotecas Digitais, disponíveis em https://digitalis.uc.pt/pt-pt/termos. Conforme exposto nos referidos Termos e Condições de Uso, o descarregamento de títulos de acesso restrito requer uma licença válida de autorização devendo o utilizador aceder ao(s) documento(s) a partir de um endereço de IP da instituição detentora da supramencionada licença. Ao utilizador é apenas permitido o descarregamento para uso pessoal, pelo que o emprego do(s) título(s) descarregado(s) para outro fim, designadamente comercial, carece de autorização do respetivo autor ou editor da obra. Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso.

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Advanced Predictive Models for Natural Disasters

Firoozi,

Firoozi

2024

Neuromorphic Computing

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Numerical modeling of wildland surface fire propagation by evolving surface curves

Cited by 13 publications

References 27 publications

Semi-automatic segmentation of NATURA 2000 habitats in Sentinel-2 satellite images by evolving open curves

Semi-automatic segmentation of NATURA 2000 habitats in Sentinel-2 satellite images by evolving open curves

New Lagrangian surface forest fire propagation model

Advanced Predictive Models for Natural Disasters

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