Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to pred...Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.展开更多
Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone...Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone imagery detection,most models still struggle with small object detection due to challenges such as object size,complex backgrounds.To address these issues,we propose a robust detection model based on You Only Look Once(YOLO)that balances accuracy and efficiency.The model mainly contains several major innovation:feature selection pyramid network,Inner-Shape Intersection over Union(ISIoU)loss function and small object detection head.To overcome the limitations of traditional fusion methods in handling multi-level features,we introduce a Feature Selection Pyramid Network integrated into the Neck component,which preserves shallow feature details critical for detecting small objects.Additionally,recognizing that deep network structures often neglect or degrade small object features,we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets.To effectively model both local and global dependencies,we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure,thereby improving feature enhancement.Furthermore,we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy.Experimental results show that,compared to the baseline model,the proposed method significantly improves small object detection performance on the VisDrone2019 dataset,with mAP@50 increasing by 4.9%and mAP@50-95 rising by 6.7%.This model also outperforms other state-of-the-art algorithms,demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.展开更多
Introduction: This article describes some possibilities of drone applications for pre-venting and responding hazardous materials disasters. Methods: Apart from reviewing the little professional literature available, t...Introduction: This article describes some possibilities of drone applications for pre-venting and responding hazardous materials disasters. Methods: Apart from reviewing the little professional literature available, the author relied on his own practical expe-rience and adopted other researchers’ related findings. He also applied logical reason-ing, systematization as well as adopting an economic approach—to assess efficiency. Results: There are two basic possibilities for the use of drones in the field of chemical disasters: one is for prevention to support the work of authorities, while the other is connected to the response to accidents or disasters. To summarize the research find-ings, the author explored the typical possibilities for use, illustrating with actual exam-ples to prove their usefulness, identified certain risks and made recommendations on further researches.展开更多
Extraction of impervious surfaces is one of the necessary processes in urban change detection.This paper derived a unified conceptual model (UCM) from the vegetation-impervious surface-soil (VIS) model to make the ext...Extraction of impervious surfaces is one of the necessary processes in urban change detection.This paper derived a unified conceptual model (UCM) from the vegetation-impervious surface-soil (VIS) model to make the extraction more effective and accurate.UCM uses the decision tree algorithm with indices of spectrum and texture,etc.In this model,we found both dependent and independent indices for multi-source satellite imagery according to their similarity and dissimilarity.The purpose of the indices is to remove the other land-use and land-cover types (e.g.,vegetation and soil) from the imagery,and delineate the impervious surfaces as the result.UCM has the same steps conducted by decision tree algorithm.The Landsat-5 TM image (30 m) and the Satellite Probatoire d’Observation de la Terre (SPOT-4) image (20 m) from Chaoyang District (Beijing) in 2007 were used in this paper.The results show that the overall accuracy in Landsat-5 TM image is 88%,while 86.75% in SPOT-4 image.It is an appropriate method to meet the demand of urban change detection.展开更多
Earthquakes pose a significant threat to life and property worldwide.Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts.This study investigates the feasibility of emp...Earthquakes pose a significant threat to life and property worldwide.Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts.This study investigates the feasibility of employing deep learning models for damage detection using drone imagery.We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8(You Only Look Once)and Detectron2.Our evaluation,based on various metrics including mAP,mAP50,and recall,demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery,particularly for cases with moderate bounding box overlap.This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency.Furthermore,to enhance real-world feasibility,we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing:frame splitting and threading.In addition,we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms,such as drones.This work contributes to the field of earthquake damage detection by(1)demonstrating the effectiveness of deep learning models,including adapted architectures,for damage detection from drone imagery,(2)highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements,and(3)proposing methods for ensemble model processing and model optimization to enhance real-world feasibility.The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation,rescue efforts,and recovery operations in the aftermath of earthquakes.展开更多
基金supported by the National Natural Science Foundation of China(41977215)。
文摘Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.
文摘Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone imagery detection,most models still struggle with small object detection due to challenges such as object size,complex backgrounds.To address these issues,we propose a robust detection model based on You Only Look Once(YOLO)that balances accuracy and efficiency.The model mainly contains several major innovation:feature selection pyramid network,Inner-Shape Intersection over Union(ISIoU)loss function and small object detection head.To overcome the limitations of traditional fusion methods in handling multi-level features,we introduce a Feature Selection Pyramid Network integrated into the Neck component,which preserves shallow feature details critical for detecting small objects.Additionally,recognizing that deep network structures often neglect or degrade small object features,we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets.To effectively model both local and global dependencies,we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure,thereby improving feature enhancement.Furthermore,we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy.Experimental results show that,compared to the baseline model,the proposed method significantly improves small object detection performance on the VisDrone2019 dataset,with mAP@50 increasing by 4.9%and mAP@50-95 rising by 6.7%.This model also outperforms other state-of-the-art algorithms,demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.
文摘Introduction: This article describes some possibilities of drone applications for pre-venting and responding hazardous materials disasters. Methods: Apart from reviewing the little professional literature available, the author relied on his own practical expe-rience and adopted other researchers’ related findings. He also applied logical reason-ing, systematization as well as adopting an economic approach—to assess efficiency. Results: There are two basic possibilities for the use of drones in the field of chemical disasters: one is for prevention to support the work of authorities, while the other is connected to the response to accidents or disasters. To summarize the research find-ings, the author explored the typical possibilities for use, illustrating with actual exam-ples to prove their usefulness, identified certain risks and made recommendations on further researches.
基金supported by the National Natural Science Foundation of China (Grant No.40671127)the National Hi-Tech Research and Development Program of China ("863" Project) (Grant Nos.2006AA120101,2006AA120102)+1 种基金the National Key Technology Research and Development Program (Grant No.2008BAK49B04)the National China Next General Internet Program (Grant No.CNGI–09–01–07)
文摘Extraction of impervious surfaces is one of the necessary processes in urban change detection.This paper derived a unified conceptual model (UCM) from the vegetation-impervious surface-soil (VIS) model to make the extraction more effective and accurate.UCM uses the decision tree algorithm with indices of spectrum and texture,etc.In this model,we found both dependent and independent indices for multi-source satellite imagery according to their similarity and dissimilarity.The purpose of the indices is to remove the other land-use and land-cover types (e.g.,vegetation and soil) from the imagery,and delineate the impervious surfaces as the result.UCM has the same steps conducted by decision tree algorithm.The Landsat-5 TM image (30 m) and the Satellite Probatoire d’Observation de la Terre (SPOT-4) image (20 m) from Chaoyang District (Beijing) in 2007 were used in this paper.The results show that the overall accuracy in Landsat-5 TM image is 88%,while 86.75% in SPOT-4 image.It is an appropriate method to meet the demand of urban change detection.
文摘Earthquakes pose a significant threat to life and property worldwide.Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts.This study investigates the feasibility of employing deep learning models for damage detection using drone imagery.We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8(You Only Look Once)and Detectron2.Our evaluation,based on various metrics including mAP,mAP50,and recall,demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery,particularly for cases with moderate bounding box overlap.This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency.Furthermore,to enhance real-world feasibility,we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing:frame splitting and threading.In addition,we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms,such as drones.This work contributes to the field of earthquake damage detection by(1)demonstrating the effectiveness of deep learning models,including adapted architectures,for damage detection from drone imagery,(2)highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements,and(3)proposing methods for ensemble model processing and model optimization to enhance real-world feasibility.The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation,rescue efforts,and recovery operations in the aftermath of earthquakes.
