Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In the last few years, the construction of aerial person Re-ID datasets has been appealing because visual surveillance through unmanned aerial vehicle (UAV) platforms has become very valuable in real-world scenarios. However, great differences are exhibited between the pedestrian images captured by ground cameras and those captured by UAVs. Person Re-ID methods based on ground person images have difficulty when performing Re-ID based on aerial person images. In this paper, we first propose a novel meta-transfer learning method for person Re-ID in aerial imagery; this approach trains a generalisable Re-ID model to learn discriminative feature representations for aerial person images. Specifically, a meta-learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilise and further improve the acquired meta-knowledge. To overcome the convergence speed and recognition accuracy reductions caused by the presence of difficult categories in the given dataset, we propose a learning strategy based on curriculum sampling that is harmonised with our meta-transfer learning framework. In addition, a new metric formulation of sample similarity is introduced based on Mahalanobis distance to improve the optimisation of the model. Extensive comparative evaluation experiments are conducted on the large-scale aerial Re-ID dataset, and the results obtained show that our method achieves a Rank-1 accuracy of 63.63 $$\%$$ and a mean average precision (mAP) of 38.02 $$\%$$ , demonstrating its potential for completing person Re-ID in aerial images. Ablation studies also validate that each component contributes to improving the performance of the model.

This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images.

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI UAV and growth parameters was shown. Correlation analysis between NDVI UAV and barley growth parameters revealed that NDVI UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI UAV , the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI UAV was applied to power function. From these results, NDVI UAV can be used as a new tool for monitoring barley growth.

In Unmanned Aerial Vehicle (UAV) networks, multi-class aerial image classification (AIC) is crucial in various applications, from environmental monitoring to infrastructure inspection. Deep Learning (DL), a powerful tool in artificial intelligence (AI), proves significant in this context, enabling the model to analyze and classify complex aerial images effectually. By utilizing advanced neural network architectures, such as convolutional neural networks (CNN), DL models outperform at identifying complex features and patterns within the aerial imagery. These models can extract spectral and spatial information from the captured data, classifying diverse terrains, structures, and objects precisely. Furthermore, the integration of Snake Optimization algorithms assists in fine-tuning the classification process, improving accuracy. As UAV networks continue to expand, DL-powered multi-class AIC significantly enhances the performance of surveillance, reconnaissance, and remote sensing tasks, contributing to the advancement of autonomous aerial systems. This study proposes a Snake Optimization Algorithm with Deep Learning for Multi-Class Aerial Image Classification (SOADL-MCAIC) methodology on UAV Networks. The main purpose of SOADL-MCAIC methodology is to recognize the presence of multiple classes of aerial images on the UAV networks. To accomplish this, the SOADL-MCAIC technique utilizes Gaussian filtering (GF) for pre-processing. In addition, the SOADL-MCAIC technique employs the Efficient DenseNet model to learn difficult and intrinsic features in the image. The SOA-based hyperparameter tuning process is used to enhance the performance of the Efficient DenseNet technique. Finally, the kernel extreme learning machine (KELM)-based classification algorithm is implemented to identify and classify the presence of various classes in aerial images. The simulation outcomes of the SOADL-MCAIC method are examined under the UCM land use dataset. The experimental analysis of the SOADL-MCAIC method portrayed a superior accuracy value of 99.75% over existing models.

Disasters are major events that have significant effects on individuals, communities, and health care systems. Nurses are valuable resources that have a role in disaster response. However, barriers in disaster education in nursing programs negatively affect nurses' knowledge and skills in disaster preparedness and response. A literature review was conducted to better understand the disaster response educational needs of nurses and to explore gaps in nurses' preparedness for disaster response and nursing education and training. Findings indicated disaster response education is a vital component of nursing education. When provided with effective disaster education, nurses are better equipped for disaster management and disaster response. Challenges in educational needs continue, and nurses lack specific disaster knowledge and preparedness. Further research is needed to identify effective teaching strategies for disaster response and preparedness. Emphasis should be placed on integrating disaster education into nursing education programs at all levels. [J Nurs Educ. 2025;64(4):235-238.].

Assessment of U.S. helicopter emergency medical services' planning and preparedness for disaster response

Small-target detection in Unmanned Aerial Vehicle (UAV) aerial images remains a significant and unresolved challenge in aerial image analysis, hampered by low target resolution, dense object clustering, and complex, cluttered backgrounds. In order to cope with these problems, we present AeroLight, a novel and efficient detection architecture that achieves high-fidelity performance in resource-constrained environments. AeroLight is built upon three key innovations. First, we have optimized the feature pyramid at the architectural level by integrating a high-resolution head specifically designed for minute object detection. This design enhances sensitivity to fine-grained spatial details while streamlining redundant and computationally expensive network layers. Second, a Dynamic Feature Fusion (DFF) module is proposed to adaptively recalibrate and merge multi-scale feature maps, mitigating information loss during integration and strengthening object representation across diverse scales. Finally, we enhance the localization precision of irregular-shaped objects by refining bounding box regression using a Shape-IoU loss function. AeroLight is shown to improve mAP50 and mAP50-95 by 7.5% and 3.3%, respectively, on the VisDrone2019 dataset, while reducing the parameter count by 28.8% when compared with the baseline model. Further validation on the RSOD dataset and Huaxing Farm Drone dataset confirms its superior performance and generalization capabilities. AeroLight provides a powerful and efficient solution for real-world UAV applications, setting a new standard for lightweight, high-precision object recognition in aerial imaging scenarios.

This study developed a machine learning-based framework for assessing roadway vulnerability and impacts in hurricane-prone regions, utilizing remote sensing techniques. To quantify the immediate and consistent impacts of hurricanes on the roadway network, the study developed two key metrics: the road closure impact index (RCII) and the roadway vulnerability index (RVI). The RCII assesses the severity of roadway closures by analyzing detected bounding boxes from high-resolution aerial imagery, offering insight into the spatial extent and severity of disruptions caused by each storm. In contrast, the RVI evaluates the consistency of roadway closure patterns across multiple events, revealing vulnerabilities within the transportation infrastructure through geospatial analysis. Also, by leveraging aerial imagery, remote sensing technology, and advanced machine learning models, the study assessed the impacts of Hurricanes Idalia and Debby on Taylor County, Florida, effectively classifying county roadway conditions in their aftermath into three categories: open, partially closed, and fully closed. Findings indicate that Hurricane Idalia caused significant structural damage due to wind and storm surge, while Hurricane Debby led to prolonged flooding and subsequent road submersion. By comparing the impacts of these two hurricanes, the study highlights the critical role of integrating machine learning, geospatial analysis, and remote sensing for enhanced disaster preparedness and response strategies. Ultimately, this framework provides critical insights for improving infrastructure resilience and planning efforts in coastal communities vulnerable to extreme weather events.

Leaf nitrogen (N) directly correlates to chlorophyll production, affecting crop growth and yield. Farmers use soil plant analysis development (SPAD) devices to calculate the amount of chlorophyll present in plants. However, monitoring large-scale crops using SPAD is prohibitively time-consuming and demanding. This paper presents an unmanned aerial vehicle (UAV) solution for estimating leaf N content in rice crops, from multispectral imagery. Our contribution is twofold: (i) a novel trajectory control strategy to reduce the angular wind-induced perturbations that affect image sampling accuracy during UAV flight, and (ii) machine learning models to estimate the canopy N via vegetation indices (VIs) obtained from the aerial imagery. This approach integrates an image processing algorithm using the GrabCut segmentation method with a guided filtering refinement process, to calculate the VIs according to the plots of interest. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). Correlations were obtained by comparing our methods against an assembled ground-truth of SPAD measurements. The higher N correlations were achieved with NN: 0.98 (V), 0.94 (R), and 0.89 (Ri). We claim that the proposed UAV stabilization control algorithm significantly improves on the N-to-SPAD correlations by minimizing wind perturbations in real-time and reducing the need for offline image corrections.

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field – often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8%. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3% and Sentinel-2 a mean overall accuracy of 83.6%. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4%. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.

High-resolution maps of surface rupturing earthquakes are essential tools for quantifying rupture hazard, understanding the mechanics of rupture propagation, and interpreting evidence of past earthquakes in the landscape. We present highly detailed maps of five portions of the surface rupture of the 2019 Ridgecrest earthquakes, derived from 5 cm per pixel aerial imagery and 2–20 cm per pixel unmanned aerial vehicle imagery. Our high-resolution maps cover areas of complexity and distributed deformation, sections in which strain is very localized, and areas where the rupture breaks through sediment and bedrock, ensuring sampling of the diverse rupture styles of this earthquake sequence. These maps reveal the near-field deformation of the surface rupture with a high level of detail, resolving the extent of secondary fracturing, lateral spreading, and liquefaction features that are below the resolution of airborne lidar data, field mapping, and geodesy. These data may serve as a machine learning training dataset, and offer opportunities for detailed kinematic analysis and high-resolution probabilistic displacement hazard analysis.

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.

Many archeological sites have been destroyed totally or partially around the globe due to earth quakes, floods and/or fire. It is the need of time to digitally preserve the historical sites before they get destroyed. It is difficult to obtain data using conventional methods due to difficulty in accessing the historical sites because of their location and neighborhood. Recent innovations in Unmanned Aerial Systems (UASs) have enabled to remote sense and acquire data using aerial imagery. In this research photogrammetry is used to develop highly accurate photo realistic 3D models. This method contributes in terms of quality, time and investment to acquire data compared to other methods which demand more time and investment. The automated flight stability and altitude control of the UAS and the fully stabilized and calibrated RGB sensor helps deliver high quality data to provide reliable results. Using aerial data acquired from a low cost and light weight Unmanned Aerial Vehicles (UAV) equipped with high resolution RGB sensor and image processing using photogrammetry tools highly accurate 3D model is formed. The main goals of this study were to conduct survey of a historical site in Pakistan utilizing advanced robotics and photogrammetry stitching techniques to investigate the potential of UAV in producing geo-referenced 3D reconstruction. The study is carried out to achieve an accuracy of within 5cm/pixel of the 3D model. The digitally preserved 3D models will help to maintain and reconstruct and historical site in case of any damage. The 3D model produced can be quickly produced for inventory, architectural and facility management system.

Roadway surface type classification (e.g., unpaved, asphalt, concrete) is an essential data element for road safety analysis and required as part of the Model Inventory of Roadway Elements (MIRE) Fundamental Data Elements (FDEs). Supplementary guidance issued by the Federal Highway Administration (FHWA) requires detailed surface type classification for the National Highway System (NHS) and roads functionally classified as interstates; however, less detailed surface type attribute group values (i.e., unpaved surface, asphalt pavement, concrete pavement, and other paved surface) are required for all remaining road segments that are not a part of the NHS. As part of a FHWA data analysis technical assistance (DATA) team, the research team worked with the Kansas Department of Transportation (KDOT) to develop a machine learning model capable of initially categorizing roads to reduce the level of effort required to satisfy the full MIRE FDE requirements. The research team conducted a pilot image classification analysis and determined a potential workflow for a broader statewide study area. This paper outlines the pilot methodologies and results. The team developed a support vector machine (SVM) learning model combined with Bayesian statistics that classified roads as paved or unpaved from aerial imagery with an overall model accuracy of 95%.

If disaster responses vary in their effectiveness across communities, health equity is affected. This paper aims to evaluate and describe variation in the federal disaster responses to 2017 Hurricanes Harvey,...