Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

Experimental Design of a Prescribed Burn Instrumentation

Observational data collected during experiments, such as the planned Fire and Smoke Model Evaluation Experiment (FASMEE), are critical for progressing and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM into operational use. Historical meteorological data, representing typical weather conditions for the anticipated burn locations and times, have been processed to initialize and run a set of simulations representing the planned experimental burns. Based on an analysis of these numerical simulations, this paper provides recommendations on the experimental setup that include the ignition procedures, size and duration of the burns, and optimal sensor placement. New techniques are developed to initialize coupled fire-atmosphere simulations with weather conditions typical of the planned burn locations and time of the year. Analysis of variation and sensitivity analysis of simulation design to model parameters by repeated Latin Hypercube Sampling are used to assess the locations of the sensors. The simulations provide the locations of the measurements that maximize the expected variation of the sensor outputs with the model parameters.Comment: 35 pages, 4 tables, 28 figure

FLOW VISUALIZATION OF BUOYANT INSTABILITY IN A CROSS-FLOW: AN IMPLICATION FOR FLAME SPREAD OVER FOREST FUEL BEDS

This thesis reports small-scale laboratory experiments designed to visualize the flow over a heated plate. A low-speed wind tunnel was built, and a heating plate was flush mounted on the wind tunnel floor to provide a uniform heat flux over its surface. A paper thin cloth soaked with commercially available Vaseline was placed on top of the heating plate to produce thick smoke streaks that were carried downstream by a horizontal airflow. Both LED light and a laser sheet of approximately 30-degrees open angle were separately used to illuminate this flow, the latter advanced downstream with 1-cm interval from the heated plate’s upstream edge. A camera with full-frame CMOS sensor recorded time series of flow patterns from four different angles. From these images, the following four flow structures were identified: (1) organized horizontal flow of vortex tubes, (2) weak vortex tubes interactions, (3) strong vortex tubes interactions (transition regime), (4) chaotic turbulent flow. Flow structure analysis showed that smoke flow height increased with horizontal distance from the heated plate and reduced with flow velocity. Scaling analysis was conducted to assess the validity of observed scale model flow structure to the USDA Forest Service medium scale wind tunnel burns

Scaling realistic fire scenarios

A review is made of work on scale modeling in fire and presented from the experience of the author. Primarily, scale modeling in air is discussed but there is a brief discussion of a scale model with salt and fresh water for smoke movement. A complete set of dimensionless groups is presented for fire, then it is illustrated how selections were made for the partial scaling of specific fire scenarios. Studies have been motivated by basic research interests as well as for fire investigations. The dynamics of floorcovering fire spread in a corridor is studied to reveal many features of fire behavior and validation is made with full-scale. Smoke movement in a department store atrium is studied to reveal flaws in the fire suppression system. The challenge was to develop a water mist system that passed fire testing, and was systematically done using a scale model and confirmed at full-scale. Fire effects on steel structures were studied at various scales, and a related classroom project examined one floor of the World Trade Center collapse on September 9, 2001. Finally, scaling was examined for a fire development in a furnished bedroom, pushing the limits of modeling to its utmost but finding some success in illustrating very similar overall behavior