Here are presented all the scientific papers published during the e-OUTLAND project that were the result of the research carried out within its framework. In particular, it includes brief descriptions of the specific research works related to the project and its educational activities, such as environmental/biodiversity protection, wildland fire management, detection and identification of extreme events (e.g. fires and floods). based on state-of-the-art methodologies and technologies.
Fire detection from images using faster R-CNN and multidimensional texture analysis.
In this paper a novel image-based fire detection approach is proposed, which combines the power of modern deep learning networks with multidimensional texture analysis based on higher-order linear dynamical systems. The candidate fire regions are identified by a Faster R-CNN network trained for the task of fire detection using a set of annotated images containing actual fire as well as selected negatives. The candidate fire regions are projected to a Grassmannian space and each image is represented as a cloud of points on the manifold. Finally, a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. For evaluating the performance of the proposed methodology, we performed experiments with annotated images of two different databases containing fire and fire-coloured objects. Experimental results demonstrate the potential of the proposed methodology compared to other state of the art approaches
Link: https://ieeexplore.ieee.org/iel7/8671773/8682151/08682647.pdf
A review on early forest fire detection systems using optical remote sensing.
The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection
algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of
fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.
Early fire detection based on aerial 360-degree sensors, deep convolution neural networks and exploitation of fire dynamic textures.
The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.
Link: https://www.mdpi.com/2072-4292/12/19/3177
Fire detection – 360-degree Dataset: https://zenodo.org/record/3736280#.Y34fgMdByUm
