Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.

The wide application and rapid development of satellite remote sensing technology have put higher requirements on remote sensing image segmentation methods. Because of its characteristics of large image size, large data volume, and complex segmentation background, not only are the traditional image segmentation methods difficult to apply effectively, but the image segmentation methods based on deep learning are faced with the problem of extremely unbalanced data between categories. In order to solve this problem, first of all, according to the existing effective sample theory, the effective sample calculation method in the context of semantic segmentation is firstly proposed in the highly unbalanced dataset. Then, a dynamic weighting method based on the effective sample concept is proposed, which can be applied to the semantic segmentation of remote sensing images. Finally, the applicability of this method to different loss functions and different network structures is verified on the self-built Landsat8-OLI remote sensing image-based tri-classified forest fire burning area dataset and the LoveDA dataset, which is for land-cover semantic segmentation. It has been concluded that this weighting algorithm can enhance the minimal-class segmentation accuracy while ensuring that the overall segmentation performance in multi-class segmentation tasks is verified in two different semantic segmentation tasks, including the land use and land cover (LULC) and the forest fire burning area segmentation In addition, this proposed method significantly improves the recall of forest fire burning area segmentation by as much as about 30%, which is of great reference value for forest fire research based on remote sensing images.

Forest fires have become a significant global threat, with many negative impacts on human habitats and forest ecosystems. This study proposed a forest fire identification method by fusing visual and infrared images, addressing the high false alarm and missed alarm rates of forest fire monitoring using single spectral imagery. A dataset suitable for image fusion was created using UAV aerial photography. An improved image fusion network model, the FF-Net, incorporating an attention mechanism, was proposed. The YOLOv5 network was used for target detection, and the results showed that using fused images achieved a higher accuracy, with a false alarm rate of 0.49% and a missed alarm rate of 0.21%. As such, using fused images has greater significance for the early warning of forest fires.

Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of forest fire spread prediction are constrained by their ability to process multidimensional fire-related data, particularly in the integration of spatiotemporal information. To address these limitations and enhance the accuracy of forest fire spread prediction, we proposed the AutoST-Net model. This innovative encoder–decoder architecture combines a three-dimensional Convolutional Neural Network (3DCNN) with a transformer to effectively capture the dynamic local and global spatiotemporal features of forest fire spread. The model also features a specially designed attention mechanism that works to increase predictive precision. Additionally, to effectively guide the firefighting work in the southwestern forest regions of China, we constructed a forest fire spread dataset, including forest fire status, weather conditions, terrain features, and vegetation status based on Google Earth Engine (GEE) and Himawari-8 satellite. On this dataset, compared to the CNN-LSTM combined model, AutoST-Net exhibits performance improvements of 5.06% in MIou and 6.29% in F1-score. These results demonstrate the superior performance of AutoST-Net in the task of forest fire spread prediction from remote sensing images.

Forest fires are significant disturbances to ecosystems, necessitating accurate mapping of burned areas and assessment of burn severity. Firstly, we reconstruct a dataset whose label uses a more flexible classification method from Landsat imagery and establish auxiliary environmental datasets for fire-affected regions. Leveraging vegetation change pre-fire and post-fire, we propose a transformer-based change detection model that integrates remote sensing and environmental information effectively. We introduce a multi-level feature fusion mechanism to address spatial resolution degradation in burn severity estimation. Experimental results show our model closely approximates evaluation dataset labels. For burned area segmentation, our method achieves the highest F1 (0.897) and mIoU of 0.781. For burn severity estimation, our method also achieves the highest mIoU (0.851). Incorporating auxiliary features improves performance by nearly 30%, while the multi-level feature fusion mechanism reduces resolution degradation by 9.6%.

When using deep reinforcement learning algorithms for path planning of a multi-DOF fruit-picking manipulator in unstructured environments, it is much too difficult for the multi-DOF manipulator to obtain high-value samples at the beginning of training, resulting in low learning and convergence efficiency. Aiming to reduce the inefficient exploration in unstructured environments, a reinforcement learning strategy combining expert experience guidance was first proposed in this paper. The ratios of expert experience to newly generated samples and the frequency of return visits to expert experience were studied by the simulation experiments. Some conclusions were that the ratio of expert experience, which declined from 0.45 to 0.35, was more effective in improving learning efficiency of the model than the constant ratio. Compared to an expert experience ratio of 0.35, the success rate increased by 1.26%, and compared to an expert experience ratio of 0.45, the success rate increased by 20.37%. The highest success rate was achieved when the frequency of return visits was 15 in 50 episodes, an improvement of 31.77%. The results showed that the proposed method can effectively improve the model performance and enhance the learning efficiency at the beginning of training in unstructured environments. This training method has implications for the training process of reinforcement learning in other domains.