46 research outputs found
A Brief Review of Machine Learning Algorithms in Forest Fires Science
Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power
VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts
Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9
Workshops Stream 1 10
Workshop Stream 2 11
Workshop Stream 3 12
Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14
Session 2 – Visualisation, communication & Teaching 27
Session 3 – Applying Machine Learning in Geosciences 36
Session 4 – Digital Outcrop Characterisation & Analysis 49
Session 5 – Airborne & Remote Mapping 58
Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69
Session 7 – Applications in Hydrology & Ecology 82
Poster Contributions 9
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
The wildfire dataset: enhancing deep learning-based forest fire detection with a diverse evolving open-source dataset focused on data representativeness and a novel multi-task learning approach
This study explores the potential of RGB image data for forest fire detection using deep learning models, evaluating their advantages and limitations, and discussing potential integration within a multi-modal data context. The research introduces a uniquely comprehensive wildfire dataset, capturing a broad array of environmental conditions, forest types, geographical regions, and confounding elements, aiming to reduce high false alarm rates in fire detection systems. To ensure integrity, only public domain images were included, and a detailed description of the dataset’s attributes, URL sources, and image resolutions is provided. The study also introduces a novel multi-task learning approach, integrating multi-class confounding elements within the framework. A pioneering strategy in the field of forest fire detection, this method aims to enhance the model’s discriminatory ability and decrease false positives. When tested against the wildfire dataset, the multi-task learning approach demonstrated significantly superior performance in key metrics and lower false alarm rates compared to traditional binary classification methods. This emphasizes the effectiveness of the proposed methodology and the potential to address confounding elements. Recognizing the need for practical solutions, the study stresses the importance of future work to increase the representativeness of training and testing datasets. The evolving and publicly available wildfire dataset is anticipated to inspire innovative solutions, marking a substantial contribution to the fieldPostprint (published version
Deep Learning approach applied to drone imagery for the automatic detection of forest fire
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWildfires are one of the world's most costly and deadly natural disasters, damaging millions
of hectares of vegetation and threatening the lives of people and animals. The risks to civilian
agents and task forces are particularly high, which emphasizes the value of leveraging
technology to minimize their impacts on nature and people. The use of drone imagery coupled
with deep learning for automated fire detection can provide new solutions to this problem,
limiting the damage that result.
In this context, our work aims to implement a solution for the automatic detection of forest
fires in real time by exploiting convolutional neural networks (CNN) on drone images based
on classification and segmentation models.
The methodological approach followed in this study can be broken down into three main
steps: First, the comparison of two models, namely Xception Network and EfficientNetB2, for
the classification of images captured during a forest burn into 'Fire' or 'No_Fire' classes. Then
we will proceed to the segmentation of the images belonging to the 'Fire' class by comparing
the U-Net architecture with Attention U-Net and Trans U-Net in order to choose the best
performing model.
The EfficientNetB2 architecture for classification gave satisfactory results with an accuracy of
71.72%. Concerning segmentation, we adopted the U-Net model which offers a segmentation
accuracy that reaches 98%. As for the deployment, a fire detection application was designed
using Android Studio software by assimilating the drone's camera
Analysis of Machine Learning Methods for Wildfire Security Monitoring with an Unmanned Aerial Vehicles
The article is about the methods of machine learning, designed for the detection of wildfires using unmanned aerial vehicles. In the article presented the review of machine learning methods, described the motivation part of machine learning usage and comparison of fire and smoke detection is made. The research was focused on machine learning application for monitoring task with a restrictions according to scenarios of a real monitoring. The results of experiments with demonstration of effectiveness of detection are presented in the conclusion part
Object Tracking Based on Satellite Videos: A Literature Review
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively
Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video
Real-time flame detection is crucial in video based surveillance systems. We
propose a vision-based method to detect flames using Deep Convolutional
Generative Adversarial Neural Networks (DCGANs). Many existing supervised
learning approaches using convolutional neural networks do not take temporal
information into account and require substantial amount of labeled data. In
order to have a robust representation of sequences with and without flame, we
propose a two-stage training of a DCGAN exploiting spatio-temporal flame
evolution. Our training framework includes the regular training of a DCGAN with
real spatio-temporal images, namely, temporal slice images, and noise vectors,
and training the discriminator separately using the temporal flame images
without the generator. Experimental results show that the proposed method
effectively detects flame in video with negligible false positive rates in
real-time