Balakavi Sai, Vadrevu Vineet, Lasko Kristofer
Universities Space Research Association (USRA) Science and Technology Institute, Huntsville, Alabama, United States of America.
The University of Alabama in Huntsville, Huntsville, Alabama, United States of America.
PLoS One. 2025 Jul 16;20(7):e0327125. doi: 10.1371/journal.pone.0327125. eCollection 2025.
Burnt area (BA) mapping is crucial for assessing wildfire impact, guiding restoration efforts, and improving fire management strategies. Accurate BA data helps estimate carbon emissions, biodiversity loss, and land surface properties post-fire changes. In this study, we designed and evaluated two deep learning-based architectures, a Custom UNET and a novel UNET-Gated Recurrent Unit (GRU), for burnt area classification using PlanetScope data over Bandipur, India. Both models demonstrated high accuracy in classifying burnt and unburnt areas. Performance metrics, including Precision, Recall, F1-Score, Accuracy, Mean Intersection over Union (IoU), and Dice Coefficient, revealed that the UNET-GRU hybrid consistently outperformed the Custom UNET, particularly in Recall and spatial overlap metrics. The Receiver Operating Characteristic (ROC) curve indicated excellent classification performance for both models, with the UNET-GRU achieving a higher AUC (0.98) compared to the Custom UNET (0.96). These findings highlight the UNET-GRU's enhanced capacity to handle finer distinctions and capture spatial and contextual features, making it a robust choice for burnt area classification in the study area. While both models avoided overfitting and maintained generalizability, integrating GRU into the UNET architecture proved particularly effective for precise classification and spatial accuracy. Our results highlight the potential of the novel UNET-GRU for burnt area mapping using very high-resolution data.
火烧面积（BA）制图对于评估野火影响、指导恢复工作以及改进火灾管理策略至关重要。准确的火烧面积数据有助于估算碳排放、生物多样性损失以及火灾后地表属性的变化。在本研究中，我们设计并评估了两种基于深度学习的架构，即自定义U-Net和新型U-Net门控循环单元（GRU），用于使用印度班迪布尔地区的PlanetScope数据进行火烧面积分类。两种模型在区分火烧和未火烧区域方面均表现出高精度。包括精确率、召回率、F1分数、准确率、平均交并比（IoU）和骰子系数在内的性能指标表明，U-Net-GRU混合模型始终优于自定义U-Net，尤其是在召回率和空间重叠指标方面。接收器操作特征（ROC）曲线表明两种模型的分类性能都非常出色，与自定义U-Net（0.96）相比，U-Net-GRU的曲线下面积（AUC）更高（0.98）。这些发现突出了U-Net-GRU在处理更细微差异以及捕捉空间和上下文特征方面的增强能力，使其成为研究区域火烧面积分类的可靠选择。虽然两种模型都避免了过拟合并保持了通用性，但将GRU集成到U-Net架构中对于精确分类和空间精度特别有效。我们的结果突出了新型U-Net-GRU在使用超高分辨率数据进行火烧面积制图方面的潜力。
