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Objectives: Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning. Methods: The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset. Results: The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively. Conclusions: The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: It is difficult for the traditional traffic information acquisition method to obtain the real-time dynamic traffic flow information of large scope and full coverage. With urbanization, the increasing number of supertall buildings makes the city skyline a favorable platform for earth observation. This paper studies the urban remote sensing video production and traffic information extraction method using the urban skyline as the observation platform. Methods: First, the earth observation data shooting is carried out in the super-tall buildings. Then, the original oblique observation data is corrected by orthography, which is further fused with the satellite image to generate a large range of remote sensing video data of the city. Finally, we train a deep learning model and use it for vehicle classification and identification. The number and density of vehicles in the area are calculated based on the identification results. Results: This paper carries out data collection and processing analysis on the sightseeing floor of Ping, an Building in Shenzhen. Results show that the proposed method can produce low-cost, long-time, large-scale and high-quality urban remote sensing video. The vehicle detection based on the remote sensing video have high accuracy. Conclusions: The proposed framework can be used to effectively monitor the regional traffic flow and sever for the smart city management. Furthermore, based on its capability of long-time, high-resolution, full-coverage and real-time remote sensing, this method can also be applied to many other urban management fields, such as urban disaster emergency response, crowd monitoring, and construction site monitoring. [ABSTRACT FROM AUTHOR]

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

Objective: Deep convolutional neural network (DCNN) is widely used in automatic road extraction from high-resolution remote sensing images (HRSIs). However, the existing methods make it difficult to model the context relationship of pixels in the predicted results. To solve this problem, some studies have used fully connected conditional random field (FullCRF) to perform secondary optimization of semantic segmentation results combined with context information, but the discontinuity problem of road structure cannot be effectively improved. Methods: In order to improve the integrity of road structure, this paper proposes a short range conditional random filed (SRCRF) model combined with DCNN. SRCRF mainly includes a unary potential function based on road pre-segmentation, a binary potential function based on spectral spatial features, and a K-neighborhood mean field inference algorithm. First, the prior knowledge of road pre-segmentation results is obtained by using the powerful feature extraction capability of DCNN as the unary potential function of SRCRF. Then, the dependence of the binary potential function defined by the linear combination of Gaussian kernel functions on the surrounding nodes is modeled. The binary potential function enables the classification results to have local consistency,that is, adjacent pixels with similar spectral features have the same label. Finally, the K-neighborhood mean field inference algorithm based on the mean-field approximation inference algorithm optimizes the inference range to make full use of the spatial context information and spectral feature context information of the road, and then calculates the optimal label corresponding to each pixel based on the space and spectral feature to optimize the road accurately. The convolution method is adopted to control the inference range of SRCRF within the radius of K in order to improve the proportion of feature vectors. Results: The experimental results show that SRCRF not only alleviates the transition smoothness of FullCRF, but also alleviates the structural discontinuity and incompleteness in the road acquisition results of HRSIs. In Zimbawe-Roads dataset and Cheng-Roads dataset, F1-scores of SRCRF are increased by about 4.01% and 3.73% respectively compared with DCNN, and about 3.25% and 2.28% respectively compared with FullCRF. Conclusions: The proposed SRCRF combines the advantages of DCNN, optimizes the fully connected structure of traditional conditional random fields into the K-neighborhood structure, reduces the inference scope, and improves the proportion of feature vectors. Compared with FullCRF, SRCRF can make better use of image color features and spatial features to accurately optimize the road extraction results of deep learning output. The performance of SRCRF is improved compared with DCNN and FullCRF, and the time is shortened by one order of magnitude compared with FullCRF. In the future, we will further investigate the potential for learning Gaussian features and investigate more complex architecture of conditional random field to better capture global context information. Additionally, we are particularly interested in exploring the application potential of SRCRF in other fields, such as building extraction, vehicle extraction, lake extraction, etc. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: A method of deep learning change detection with domain knowledge as an optimization strategy was proposed to improve the change detection precision of high-resolution remote sensing images. Methods: The improved change vector analysis algorithm and grey-level co-occurrence matrix algorithm were used to obtain the spectral and texture changes of images, and reasonable thresholds were set to divide the changed samples from the unchanged samples based on the spectral and texture change intensity maps. The pattern shape index and spectral knowledge in domain knowledge were introduced as an optimization strategy to filter the changed samples for obtaining high-quality training samples. The deep belief net⁃ work model was constructed and trained, and the results of deep learning change detection were optimized by the optimization strategy to reduce the influence of “salt and pepper noise” and false change zones on the detection accuracy. Results: The Results of change detection experiments show that the accuracies of Gaofen⁃2 and IKONOS images were increased by 7.58% and 14.69% and the recall by 17.08% and 23.87%, respectively, while the false alarms and were decreased by 30.22% and 23.30% and the missing alarms by 17.08% and 23.87%, respectively. Conclusions: Compared with the method before the optimization strategy was adopted, the proposed method in this paper can effectively improve the precision of change detection, and it provides a new way of using remote sensing images to improve the precision of deep learning change detection. [ABSTRACT FROM AUTHOR]

3D reconstruction technology is widely used in digital elevation model production, robot naviga‐ tion, augmented reality and autonomous driving, etc. Disparity map is an important expression of 3D recon‐ struction, and stereo matching is the most widely used technology to obtain a disparity map. In recent years, with the development of hardware, data sets, and algorithms, stereo matching methods based on deep learning have received extensive attention and achieved great success. However, these works are mainly validated in close ‐ range images, and the evaluation on remote sensing aerial images is scarce. This paper reviews deep learning methods for stereo matching, and selects five representative models, such as GC‐Net (geometry and context network), PSM‐Net (pyramid stereo matching network), GWC‐Net (group‐ wise correlation stereo network), GA ‐Net (guided aggregation network), HSM ‐Net (hierarchical deep ste‐ reo matching network), and applies them to a set of open source street ‐ scene datasets (KITTI2015) and two sets of aerial remote sensing image datasets (München, WHU). The various networks are analyzed, and the performance of deep learning stereo matching methods is discussed and compared to traditional methods. The experimental results reveals that most of the deep learning methods exceed the classic semi‐ global matching and had a powerful generalization ability on cross‐dataset transfer. [ABSTRACT FROM AUTHOR]

Objective: The building collapse is the main cause of casualties and economic losses in all kinds of disasters. Accurate assessment of the collapse of buildings is an important basis for disaster rescue and government decision-making, and plays an important role in the proper placement of the victims, the protection of the lives and property of the victims. Therefore, the rapid assessment of building collapse after disaster is a research hotspot in the field of remote sensing and civil engineering. The main Objective: of this study was to apply the deep learning technology to automatically identify and assess building collapse and structural damage with on-site images. Methods: The building collapse assessment method used in this paper is ResNet (residual network). A ResNet-50 architecture was implemented in TensorFlow, which consists of 5 stages each with a convolution and identity block, 3 convolution layers in each convolution block and 3 convolution layers in each identity block. According to the building damage assessment standard developed by the China Earthquake Administration, the evaluation output of this model is adjusted to three types: Completely collapsed, semi-collapsed, and non-collapse. In the data augmentation process, geometry transformation and blur processing were used to simulate different shooting environments, to reduce the overfitting of the classifier model by enhancing the training image. Through data expansion, the number of training data has been increased by at least 10 times on the basis of PEER Hub ImageNet Challenge open dataset. In the process of transfer learning, the gradient descent algorithm was used to adjust and optimize the parameters of the neural network. The Results: of this method were evaluated by using accuracy and Kappa coefficient. Results: Finally, in the building collapse assessment experiment, the classification accuracy of the assessment model reached 77.23% and the Kappa coefficient was 0.65; in the structural damage assessment experiment, the classification accuracy of the assessment model reached 77% and the Kappa coefficient was 0.693.The proposed collapse classification successfully attains a relatively high rate for true-positives. Overall, this method performed well. This method shows great promise in supporting on-site decision-making for the rapid accurate assessment of building collapse after disaster. Conclusions: A novel automatic classification method for post-disaster building collapse evaluation is proposed,which meets the building damage evaluation standard. The method is demonstrated on a specific example classification focused on collapse classification and structural damage type identification.The experiment Results: show great promise in the rapid accurate assessment of building collapse after disaster. In the future we plan to improve the model to enhance the ability to extract subtle features in the image. [ABSTRACT FROM AUTHOR]