Vision based wildfire and natural risk observers

Abstract

Wildfires are natural risk phenomena that cause significant economic and environmental damage. In wildfire fighting strategy it is important to detect the wildfire in its initial stage and to apply, as soon as possible, the most appropriate fire fighting action. In both cases wildfire monitoring and surveillance systems are of great importance, so in the last decade the interest for various wildfire monitoring and surveillance systems has increased, both on the research and the implementation level. This paper describes one such system named iForestFire. It is an example of advanced terrestrial vision based wildfire monitoring and surveillance system, today widely used in various Croatian National and Nature Parks and regions, but it is also a system in constant development and improvement, both on theoretical and practical level. This paper describes its last improvements in video detection part that are based on notation of observer, cogent confabulation theory and mechanism of thought. Inclusion of cogent confabulation theory allows us to expend the use of existing wildfire observers to more general natural risk observers.

Early Wildfire Detection Technologies in Practice—A Review

“…The decoupling of the capturing and processing provides the advantage of continuous improvement and integration as researchers can update the models and analyze past performance without having to alter any of the physical components or interrupt its ongoing operation. For example, by basing their project on cloud computation [83], Darko Stipaničev et al, have continued to release improvements to their iForestFire semi-automatic monitoring system after its commercial deployment and operations in various regions.…”

“…A common weakness in smoke detection and image-based detection is deterioration of image quality at night and by the presence of haze. Haze is defined as an "an obscurity occurring due to smoke, dust, or other particles suspended in the atmosphere" [83]. This causes images and colors to become fuzzy or blurry, decreasing contrast between elements of the image and therefore increasing the difficulty of identifying smoke.…”

RS-CPS: A distributed architecture of robotic surveillance cyber-physical system in the nature environment

“…A demonstration of bird species detection using an acoustic wireless sensor network is presented [13]. For vision-based wildlife recognition and nature risk detection, in [14] the authors describe a video based terrestrial systems usign cogent confabulation based detection classifier which can make the wildfire detection a simpler process and allow users to generalize the existing wildfire observer to a more general natural risk observer. in [16] and [15], filtering algorithms for automatic bird species detection suing videos captured are presented.…”

An IoT-based electronic sniffing for forest fire detection

“…Although the introduction of cameras may facilitate human monitoring and control activities, the presence of an operator remains essential for the correct interpretation of the sensed data [6]. The latest evolution of the firefighting system, based on scene observation through cameras, is the partial replacement of the human operator with artificial intelligence (AI) tools.…”