To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm.

Fire is an important ecosystem process and has played a complex role in terrestrial ecosystems and the atmosphere environment. Sometimes, wildfires are highly destructive natural disasters. To reduce their destructive impact, wildfires must be detected as soon as possible. However, accurate and timely monitoring of wildfires is a challenging task due to the traditional threshold methods easily be suffered to the false alarms caused by small forest clearings, and the omission error of large fires obscured by thick smoke. Deep learning has the characteristics of strong learning ability, strong adaptability and good portability. At present, few studies have addressed the wildfires detection problem in remote sensing images using deep learning method in a nearly real time way. Therefore, in this research we proposed an active fire detection system using a novel convolutional neural network (FireCNN). FireCNN uses multi-scale convolution and residual acceptance design, which can effectively extract the accurate characteristics of fire spots. The proposed method was tested on dataset which contained 1,823 fire spots and 3,646 non-fire spots. The experimental results demonstrate that the FireCNN is fully capable of wildfire detection, with the accuracy of 35.2% higher than the traditional threshold method. We also examined the influence of different structural designs on the performance of neural network models. The comparison results indicates the proposed method produced the best results.

The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network—namely, the earthquake building damage classification net (EBDC-Net)—for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively.

Quickly and accurately assessing the damage level of buildings is a challenging task for post-disaster emergency response. Most of the existing research mainly adopts semantic segmentation and object detection methods, which have yielded good results. However, for high-resolution Unmanned Aerial Vehicle (UAV) imagery, these methods may result in the problem of various damage categories within a building and fail to accurately extract building edges, thus hindering post-disaster rescue and fine-grained assessment. To address this issue, we proposed an improved instance segmentation model that enhances classification accuracy by incorporating a Mixed Local Channel Attention (MLCA) mechanism in the backbone and improving small object segmentation accuracy by refining the Neck part. The method was tested on the Yangbi earthquake UVA images. The experimental results indicated that the modified model outperformed the original model by 1.07% and 1.11% in the two mean Average Precision (mAP) evaluation metrics,${mAP}_{bbox}^{50}$ and ${mAP}_{seg}^{50}$, respectively. Importantly, the classification accuracy of the intact category was improved by 2.73% and 2.73%, respectively, while the collapse category saw an improvement of 2.58% and 2.14%. In addition, the proposed method was also compared with state-of-the-art instance segmentation models, e.g., Mask-R-CNN and YOLO V9-Seg. The results demonstrated that the proposed model exhibits advantages in both accuracy and efficiency. Specifically, the efficiency of the proposed model is three times faster than other models with similar accuracy. The proposed method can provide a valuable solution for fine-grained building damage evaluation.

A timely and accurate damage assessment of buildings after an earthquake is critical for the safety of people and property. Most of the existing methods based on classification and segmentation use two-dimensional information to determine the damage level of the buildings, which cannot provide the multi-view information of the damaged building, resulting in inaccurate assessment results. According to the knowledge of the authors, there is no related research using the deep-learning-based 3D reconstruction method for the evaluation of building damage. In this paper, we first applied the deep-learning-based MVS model to reconstruct the 3D model of the buildings after an earthquake using multi-view UAV images, to assist the building damage assessment task. The method contains three main steps. Firstly, the camera parameters are calculated. Then, 3D reconstruction is conducted based on CasMVSNet. Finally, a building damage assessment is performed based on the 3D reconstruction result. To evaluate the effectiveness of the proposed method, the method was tested in multi-view UAV aerial images of Yangbi County, Yunnan Province. The results indicate that: (1) the time efficiency of CasMVSNet is significantly higher than that of other deep learning models, which can meet the timeliness requirement of post-earthquake rescue and damage assessment. In addition, the memory consumption of CasMVSNet is the lowest; (2) CasMVSNet exhibits the best 3D reconstruction result in both high and small buildings; (3) the proposed method can provide detail and multi-view information of damaged buildings, which can be used to assist the building damage assessment task. The results of the building damage assessment are very similar to the results of the field survey.

High resolution stereo satellite images (HRSSIs) have the potential to provide accurate height and volume information, playing a crucial role in assessing building collapses during various natural disasters. However, the time-consuming process of 3D reconstruction, inadequate vertical accuracy of digital surface model (DSM), and concentrated clustering of buildings pose challenges for collapse assessment focused on buildings. Therefore, we present an improved approach for rapid fine-grained assessment of building collapses. Firstly, accurate and consistent positioning parameters for HRSSIs are obtained through the combined block adjustment using laser altimetry points (LAPs), ensuring the generation of DSMs with vertical accuracy exceeding 2m. Next, a set of rapid 3D reconstruction techniques are introduced, achieving a significant eight-fold improvement in generating DSMs. Subsequently, we deploy an automated workflow for batch processing and registration of open-source building footprints, enabling accurate extraction of building height changes from dual-time DSMs. Finally, based on the building change image, a large-scale GIS image of building floor-level collapses is generated using connected component detection and threshold classification strategies. These findings have far-reaching implications for post-disaster emergency response, damage assessment, and expeditious reconstruction efforts. In our study, we processed an 800 square kilometer area in Kahramanmaras Province, Turkey, generating dual-time DSMs within one hour. This enabled the assessment of floor-level collapses for a total of 48,092 buildings within the area. Validation was conducted on 361 houses in the city center, utilizing Google Street View images as ground truth. Remarkably, our approach achieved a high accuracy rate of 93.27% in floor-level assessment.