构建超10万对遥感图像数据集，助力深度学习精准识别森林火灾。
Development and Application of a Sentinel-2 Satellite Imagery Dataset for Deep-Learning Driven Forest Wildfire Detection
- 用双时相哨兵2号影像构建带标签数据集
- 高效网络模型检测准确率超92%
- 适合从事环境监测与灾害预警的研究者
森林因自然事件（如野火）造成的损失正成为全球性挑战，亟需先进分析方法进行有效检测与应对。将卫星影像与深度学习（DL）结合已成为关键路径，但该方法依赖大量标注数据以获得高精度结果。本研究基于谷歌地球引擎（GEE）获取的双时相哨兵2号影像，构建了加州野火地理影像数据集（CWGID），包含超过10万对高分辨率的灾前灾后标注影像，用于支持深度学习驱动的森林火灾检测。数据处理流程涵盖数据获取、清洗与分析，采用三种预训练卷积神经网络（CNN）架构进行初步评估。结果表明，EF EfficientNet-B0模型在火灾检测任务中达到超过92%的准确率。CWGID及其构建方法为训练和测试深度学习模型提供了宝贵资源，可广泛支持环境监测中的其他深度学习应用。
原文摘要 · Abstract (English)
Forest loss due to natural events, such as wildfires, represents an increasing global challenge that demands advanced analytical methods for effective detection and mitigation. To this end, the integration of satellite imagery with deep learning (DL) methods has become essential. Nevertheless, this approach requires substantial amounts of labeled data to produce accurate results. In this study, we use bi-temporal Sentinel-2 satellite imagery sourced from Google Earth Engine (GEE) to build the California Wildfire GeoImaging Dataset (CWGID), a high-resolution labeled satellite imagery dataset with over 100,000 labeled before and after forest wildfire image pairs for wildfire detection through DL. Our methods include data acquisition from authoritative sources, data processing, and an initial dataset analysis using three pre-trained Convolutional Neural Network (CNN) architectures. Our results show that the EF EfficientNet-B0 model achieves the highest accuracy of over 92% in detecting forest wildfires. The CWGID and the methodology used to build it, prove to be a valuable resource for training and testing DL architectures for forest wildfire detection.
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