用预训练模型提升森林火灾检测精度，尤其适合数据少的地区。
Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand
- 用MobileNetV2等预训练模型迁移学习，减少标注数据依赖。
- 在乌塔拉坎德数据集上实现高检测准确率，优于传统方法。
- 适合资源有限、数据稀缺地区的灾害监测应用。
森林火灾对环境、人类生命和财产构成重大威胁，早期发现与响应至关重要。然而，传统检测方法常依赖人工观察或空间分辨率低的卫星影像，效果受限。本文聚焦迁移学习在印度森林火灾检测中的作用，应对数据采集困难并提升跨区域模型精度。通过对比传统方法与迁移学习，研究了地形、气候和植被差异带来的挑战。迁移学习按源任务与目标任务相似性分为多种类型，其中利用预训练模型是关键策略，显著降低对大量标注数据的需求。文中详述迁移学习流程，展示如何将MobileNetV2等模型适配至森林火灾检测任务。最后基于乌塔拉坎德森林火灾数据集进行实验，验证了迁移学习在此场景下的有效性。
原文摘要 · Abstract (English)
Forest fires pose a significant threat to the environment, human life, and property. Early detection and response are crucial to mitigating the impact of these disasters. However, traditional forest fire detection methods are often hindered by our reliability on manual observation and satellite imagery with low spatial resolution. This paper emphasizes the role of transfer learning in enhancing forest fire detection in India, particularly in overcoming data collection challenges and improving model accuracy across various regions. We compare traditional learning methods with transfer learning, focusing on the unique challenges posed by regional differences in terrain, climate, and vegetation. Transfer learning can be categorized into several types based on the similarity between the source and target tasks, as well as the type of knowledge transferred. One key method is utilizing pre-trained models for efficient transfer learning, which significantly reduces the need for extensive labeled data. We outline the transfer learning process, demonstrating how researchers can adapt pre-trained models like MobileNetV2 for specific tasks such as forest fire detection. Finally, we present experimental results from training and evaluating a deep learning model using the Uttarakhand forest fire dataset, showcasing the effectiveness of transfer learning in this context.
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