A Land Cover Recognition-Based Method for Wildfire Early Warning in Transmission Corridor Areas

To improve the accuracy of wildfire risk identification in areas adjacent to power transmission corridors, this study proposes a wildfire early warning method that integrates refined land cover segmentation and multimodal feature deep learning. First, an improved bi-branch semantic segmentation network (BuildFormer++) is used to perform refined classification of high-resolution remote sensing images, extracting six types of land cover information, including forest and cultivated land. Second, a multi-dimensional feature set integrating land cover, topography, climate, and human activities is constructed and input into a multimodal wildfire point prediction network for deep feature fusion and probabilistic modeling. Experimental results show that the proposed segmentation network achieves a mean intersection–union ratio (mIoU) of 40.68% in the semantic segmentation task; the early warning model achieves an accuracy of 85.37%, an F1 score of 93.15%, and an ROC-AUC of 85.42% in risk prediction, significantly outperforming comparative methods. The “refined segmentation–feature fusion–risk prediction” framework constructed by this method can provide reliable technical support for the operation and maintenance safety and fire prevention of power transmission corridors.