Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Multi-source Remote Sensing for Forest Characterization and Monitoring

As a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing

딥러닝을 이용한 화재 감지 및 화재 이미지 시맨틱 분할

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2020. 8. 강명주.최근 딥 러닝은 다양한 분야에서 가장 중요하고 강력한 주제이다. 딥러닝은 이미지 분류에서 뛰어난 성능을 보였으며, 이후 컴퓨터 비전의 이지미에서 객체 감지 및 시맨틱 분할에도 적용되었다. 본 논문에서는 뛰어난 성능을 가진 딥 러닝을 사용하여 화재 이미지 감지 및 분할 작업에 적합한 네트워크를 제안한다. 또한 딥러닝 압축 기법을 사용하여 화재 이미지 분할 딥러닝 모델에 적용하여 소규모 네트워크를 제안하였고 이를 임베디드 장치에 적용했다. 여러가지 광범위한 실험을 통해 화재감지와 화재 이미지 분할에서 기존의 기법보다 좋다는 점을 보였다.Recently, deep learning has become the most important and powerful topic in various research fields. It has shown excellent performance in image classification and has been applied to the fields of object detection and semantic image segmentation of computer vision. In this thesis, we proposed deep neural networks suitable for fire image detection and segmentation tasks with excellent performance. In addition, we proposed a small-sized network for fire image segmentation based on squeezed deep-learning techniques and applied it to an embedded device. Several extensive experiments are presented to demonstrate its better performance compared with the existing methods for fire detection and image segmentation.1 Introduction 1 2 Preliminaries 4 2.1 Image classification 4 2.2 Object detection 9 2.3 Semantic image segmentation 12 2.4 Compressed deep learning 15 3 Fire Detection and Localization 17 3.1 Related work 17 3.2 Proposed method 19 3.3 Experiments 21 3.3.1 Fire area localization 28 3.3.2 Fire localization results 30 3.4 Conclusion 32 4 Semantic Segmentation using Deep Learning for Fire Images 34 4.1 Related work 34 4.2 Proposed architecture 38 4.2.1 Comparison with FusionNet 40 4.3 Experimental results 41 4.3.1 Experimental results using FiSmo dataset 42 4.3.2 Experimental results using Corsican Fire Database 45 4.4 Conclusion 48 5 Squeezed Semantic Segmentation for Fire Images 49 5.1 Related work 49 5.2 Squeezed Fire Binary Segmentation Networks(SFBSNet) 50 5.2.1 SFBSNet architecture 50 5.2.2 Implementation details 54 5.3 Experiments 56 5.3.1 Ablation study 58 5.3.2 Experiments on FiSmo dataset 58 5.3.3 Experiments on Corsican Fire Database 60 5.3.4 Additional experiments on Still dataset 60 5.4 Conclusion 62 6 Conclusion and Future Works 64 Abstract (in Korean) 73Docto

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal