Parallel implementation of a simplified semi-physical wildland re spread model using OpenMP

[EN]We present a parallel 2D version of a simplified semi-physical wildland fire spread model based on conservation equations, with convection and radiation as the main heat transfer mechanisms. This version includes some 3D effects. The OpenMP framework allows distributing the prediction operations among the available threads in a multicore architecture, thereby reducing the computational time and obtaining the prediction results much more quickly. The results from the experiments using data from a real fire in Galicia (Spain) confirm the benefits of using the parallel version.Junta of Castilla y Leó

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Scenario Generation with Cellular Automaton for Game-based Crisis Simulation System

Crisis is infrequent and unpredictable event which is challenging to prepare and resolve. Scenario generation for modeling a crisis using computational approach is preferable due to its flexibility to produce automated variation of scenario based on different setup key factors. Combining crisis generation with game technology, serious-game game can be developed to provide potential support in training and simulation practice of real-world crisis situation to different stakeholders. Therefore, in this paper, we propose a computational framework to provide automated generation of a fire crisis scenario with firefighter-agent simulation using cellular automaton with influence map for agent behavior optimization, and visualization using a 3D game engine. The framework is evaluated based on performance, convergence and realistic behavior of both crisis event (e.g. fire propagation or control) and crisis resolution (e.g. firefighter solving the crisis)

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin assert joint first authorshi

Modeling strategies for multiple scenarios and fast simulations in large systems: applications to fire safety and energy engineering

The use of computational modeling has become very popular and important in many engineering and physical fields, as it is considered a fast and inexpensive technique to support and often substitute experimental analysis. In fact system design and analysis can be carried out through computational studies instead of experiments, that are typically demanding in terms of cost and technical resources; sometimes the systems characteristics and the technical problems make the experiments impossible to perform and the use of computational tools is the only feasible option. Demand of resources for realistic simulation is increasing due to the interest in studying complex and large systems. In these framework smart modeling approaches and model reduction techniques play a crucial role for making complex and large system suitable for simulations. Moreover, it should be considered that often more than one simulation is requested in order to perform an analysis. For instance, if a heuristic method is applied to the optimization of a component, the model has to be run a certain number of times. The same problem arises when a certain level of uncertainty affect the system parameters; in this case also many simulation are required for obtaining the desired information. This is the reason why the use of technique that allows to obtain compact model is an interesting topic nowadays. In this PhD thesis different reduction approaches and strategies have been used in order to analyze three energetic systems involving large domain and long time, one for each reduction approach categories. In all the topic considered, a smart model has been adopted and, when data were available, tested using experimental data. All the model are characterized by large domain and the time involved in the analysis are high in all the cases, therefore a method for compact model achievement is used in all the cases. The considered topics are: • Groundwater temperature perturbations due to geothermal heat pump installations, analyzed trough a multi-level model. • District heating networks (DHN), studied from both the fluid-dynamic and thermal point of view and applied to one of the larger network in Europe, the Turin district heating system (DHS), trough a Proper Orthogonal Decomposition - Radial Basis Function model. • Forest fire propagation simulation carried out using a Proper Orthogonal Decomposition projection model

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Integrated material practice in free-form timber structures

Integrated material practice in free-form timber structures is a practice-led research project at CITA (Centre for IT and Architecture) that develops a digitally-augmented material practice around glue-laminated timber. The project is part of the InnoChain ETN and has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642877. The advent of digital tools and computation has shifted the focus of many material practices from the shaping of material to the shaping of information. The ability to process large amounts of data quickly has made computation commonplace in the design and manufacture of buildings, especially in iterative digital design workflows. The simulation of material performance and the shift from models as representational tools to functional ones has opened up new methods of working between digital model and physical material. Wood has gained a new relevance in contemporary construction because it is sustainable, renewable, and stores carbon. In light of the climate crisis and concerns about overpopulation, and coupled with developments in adhesives and process technology, it is returning to the forefront of construction. However, as a grown and heterogeneous material, its properties and behaviours nevertheless present barriers to its utilization in architecturally demanding areas. Similarly, the integration of the properties, material behaviours, and production constraints of glue-laminated timber (glulam) assemblies into early-stage architectural design workflows remains a challenging specialist and inter-disciplinary affair. Drawing on a partnership with Dsearch – the digital research network at White Arkitekter in Sweden – and Blumer Lehmann AG – a leading Swiss timber contractor – this research examines the design and fabrication of glue-laminated timber structures and seeks a means to link industrial timber fabrication with early-stage architectural design through the application of computational modelling, design, and an interrogation of established timber production processes. A particular focus is placed on large-scale free-form glulam structures due to their high performance demands and the challenge of exploiting the bending properties of timber. By proposing a computationally-augmented material practice in which design intent is informed by material and fabrication constraints, the research aims to discover new potentials in timber architecture. The central figure in the research is the glulam blank - the glue-laminated near-net shape of large-scale timber components. The design space that the blank occupies - between sawn, graded lumber and the finished architectural component - holds the potential to yield new types of timber components and new structural morphologies. Engaging with this space therefore requires new interfaces for design modelling and production that take into account the affordances of timber and timber processing. The contribution of this research is a framework for a material practice that integrates processes of computational modelling, architectural design, and timber fabrication and acts as a broker between domains of architectural design and industrial timber production. The research identifies four different notions of feedback that allow this material practice to form