A novel approach for forest fire detection using image processing technique
is proposed. A rule-based color model for fire pixel classification is used.
The proposed algorithm uses RGB and YCbCr color space. The advantage of using
YCbCr color space is that it can separate the luminance from the chrominance
more effectively than RGB color space. The performance of the proposed
algorithm is tested on two sets of images, one of which contains fire; the
other contains fire-like regions. Standard methods are used for calculating the
performance of the algorithm. The proposed method has both higher detection
rate and lower false alarm rate. Since the algorithm is cheap in computation,
it can be used for real-time forest fire detection.

使用图像处理技术提出了一种新颖的森林火灾检测方法，该方法使用基于规则的颜色模型进行火焰像素分类，其中使用了 RGB 和 YCbCr 颜色空间，并采用标准方法测试算法性能，结果表明该方法具有更高的检测率和较低的误报率，可用于实时森林火灾检测。

基于图像处理的森林火灾检测

Image Processing Based Forest Fire Detection

Forests are crucial for ecological balance, but wildfires, a major cause of
forest loss, pose significant risks. Fire weather indices, which assess
wildfire risk and predict resource demands, are vital. With the rise of sensor
networks in fields like healthcare and environmental monitoring, semantic
sensor networks are increasingly used to gather climatic data such as wind
speed, temperature, and humidity. However, processing these data streams to
determine fire weather indices presents challenges, underscoring the growing
importance of effective forest fire detection. This paper discusses using
Apache Spark for early forest fire detection, enhancing fire risk prediction
with meteorological and geographical data. Building on our previous development
of Semantic Sensor Network (SSN) ontologies and Semantic Web Rules Language
(SWRL) for managing forest fires in Monesterial Natural Park, we expanded SWRL
to improve a Decision Support System (DSS) using a Large Language Models (LLMs)
and Spark framework. We implemented real-time alerts with Spark streaming,
tailored to various fire scenarios, and validated our approach using ontology
metrics, query-based evaluations, LLMs score precision, F1 score, and recall
measures.

利用 Apache Spark 对早期森林火灾进行检测、通过气象和地理数据提高火灾风险预测的论文进行了讨论，使用了 Semantic Sensor Networks、Large Language Models 和 Spark 框架改进了决策支持系统，并通过实时警报和多种火灾场景的验证来衡量方法的有效性。

基于本体论、大数据和 LLMs 的森林火灾管理决策支持系统

Decision support system for Forest fire management using Ontology with  Big Data and LLMs

This article delves into the analysis of performance and utilization of
Support Vector Machines (SVMs) for the critical task of forest fire detection
using image datasets. With the increasing threat of forest fires to ecosystems
and human settlements, the need for rapid and accurate detection systems is of
utmost importance. SVMs, renowned for their strong classification capabilities,
exhibit proficiency in recognizing patterns associated with fire within images.
By training on labeled data, SVMs acquire the ability to identify distinctive
attributes associated with fire, such as flames, smoke, or alterations in the
visual characteristics of the forest area. The document thoroughly examines the
use of SVMs, covering crucial elements like data preprocessing, feature
extraction, and model training. It rigorously evaluates parameters such as
accuracy, efficiency, and practical applicability. The knowledge gained from
this study aids in the development of efficient forest fire detection systems,
enabling prompt responses and improving disaster management. Moreover, the
correlation between SVM accuracy and the difficulties presented by
high-dimensional datasets is carefully investigated, demonstrated through a
revealing case study. The relationship between accuracy scores and the
different resolutions used for resizing the training datasets has also been
discussed in this article. These comprehensive studies result in a definitive
overview of the difficulties faced and the potential sectors requiring further
improvement and focus.

这篇文章主要研究了使用支持向量机（SVM）对基于图像数据集的森林火灾检测进行性能和利用率分析。通过对带标签数据进行训练，SVM 能够识别与火灾相关的特征，如火焰、烟雾或森林区域的视觉特征变化。文章深入探讨了 SVM 的使用，包括数据预处理、特征提取和模型训练等关键要素，评估了准确性、效率和实际适用性等参数。通过一个具有启示性的案例研究，文章还仔细调查了 SVM 准确性与高维数据集所带来的困难之间的关系。此外，文章还讨论了准确性分数与训练数据集调整不同分辨率之间的关系。综合研究结果揭示了所面临的困难以及需要进一步改进和关注的潜在领域。