Personnel and boat detection in unmanned aerial vehicles (UAVs) imagery plays a crucial role in open water search and rescue missions. The diverse perspectives and altitudes of UAV images often result in significant variations in the imagery’s appearance and dimensions of personnel and boats, and the false detections arising from water surface flares are acknowledged as a great challenge as well. The existing deep learning-based detection methods employ convolutional blocks with fixed kernel sizes to extract features from the imagery at a fixed spatial scale, which will lead to missed and false detections and severely affect detection accuracy when there are substantial differences in the appearance and size of the target objects. In this article, a spatial scale adaptive real-time object detection neural network, namely YoloOW, was proposed to tackle the challenge of personnel and boat detection amid the diverse UAV imagery, which comprises a feature extractor, a feature enhancer, and a postprocessor. The OaohRep convolutional block was proposed as a pivotal component in constructing the YoloOW and applied to the feature extractor and the feature enhancer. Compared with general convolution blocks, the OaohRep convolution block can extract image features across a wide range of spatial scales, show better scale adaptability, and achieve faster detection speed due to its unique merged convolution layer design. OaohRepBi-path aggregation network (PAN) was proposed in the feature enhancer, which imitated the architecture of the classic algorithm scale invariant feature transform (SIFT) and was successfully applied to deep learning models, showing better scale adaptability. A novel UAV detection box filter (UDBF) module was proposed in the postprocessor, which can effectively remove false detections caused by water surface flares. Experimental results demonstrate that our YoloOW model achieves 37.18% mean averaged precision (mAP) on the SeaDronesSee dataset, surpassing the baseline by 8.43%. This notable improvement positions our model at the first of the leaderboard. The code will be available at https://github.com/Xjh-UCAS/YoloOW .

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.

Conventional numerical methods have made significant advances in forecasting tropical cyclone (TC) tracks, using remote sensing data with high spatial and temporal resolutions. However, over the past two decades, no significant improvements have been made with regard to the accuracy of TC intensity prediction, which remains challenging, as the internal convection and formation mechanisms of such storms are not fully understood. This study investigated the relationship between remote sensing data and TC intensity to improve the accuracy of TC intensity prediction. An intensity forecast model for the South China Sea was built using the eXtreme Gradient Boosting (XGBoost) model and FengYun-2 (FY-2) satellite data, environmental data, and best track datasets from 2006 to 2017. First, correlation analysis algorithms were used to extract the TC regions in which the satellite data were best correlated, with TC intensity at lead times of 6, 12, 18, and 24 h. Then, satellite, best track, and environmental data were used as source data to develop three different XGBoost models for predicting TC intensity: model A1 (climatology and persistence predictors + environmental predictors), model A2 (A1 + satellite-based predictors extracted as mean values), and model A3 (A1 + satellite-based predictors extracted by our method). Finally, we analyzed the impact of the FY-2 satellite data on the accuracy of TC intensity prediction using the forecast skill parameter. The results revealed that the equivalent blackbody temperature (TBB) of the FY-2 data has a strong correlation with TC intensity at 6, 12, 18, and 24 h lead times. The mean absolute error (MAE) of model A3 was reduced by 0.47%, 1.79%, 1.91%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A2, respectively, and by 2.73%, 7.58%, 7.64%, and 5.04% in 6, 12, 18, and 24 h forecasts, respectively, relative to those of model A1. Furthermore, the accuracy of TC intensity prediction is improved by FY-2 satellite images, and our extraction method was found to significantly improve upon the traditional extraction method.

An Augmented virtual environment (AVE) is concerned with the fusion of real-time video with 3D models or scenes so as to augment the virtual environment. In this paper, a new approach to establish an AVE with a wide field of view is proposed, including real-time video projection, multiple video texture fusion and 3D visualization of moving objects. A new diagonally weighted algorithm is proposed to smooth the apparent gaps within the overlapping area between the two adjacent videos. A visualization method for the location and trajectory of a moving virtual object is proposed to display the moving object and its trajectory in the 3D virtual environment. The experimental results showed that the proposed set of algorithms are able to fuse multiple real-time videos with 3D models efficiently, and the experiment runs a 3D scene containing two million triangles and six real-time videos at around 55 frames per second on a laptop with 1GB of graphics card memory. In addition, a realistic AVE with a wide field of view was created based on the Digital Earth Science Platform by fusing three videos with a complex indoor virtual scene, visualizing a moving object and drawing its trajectory in the real time.

The violent and persistent wildfires that broke out along the southeast coast of Australia in 2019 caused a large number of pollutant emissions, which seriously affected air quality and the global climate. The existing two methods for estimating combustion emissions based on burned area and fire radiative power mainly use a medium resolution imaging spectrometer (MODIS) on the Aqua and Terra satellites. However, the low temporal resolution of MODIS and insensitivity to small fires lead to deviation in the estimation of fire emissions. In order to solve this problem, the Visible Infrared Imaging Radiometer Suite (VIIRS) with better performance is adopted in this paper, combined with the fire diurnal cycle information obtained by geostationary satellite Himawari-8, to explore the spatio-temporal model of biomass combustion emissions. Using this, a high-spatial- and -temporal-resolution fire emission inventory was generated for southeastern Australia from November 2019 to January 2020, which aims to fully consider the highly dynamic nature of fires and small fires (low FRP) that are much lower than the MODIS burned area or active fire detection limit, with emphasis on dry matter burned (DMB). We found that during the study period, the fire gradually moved from north to south, and the diurnal cycle of the fire in the study area changed greatly. The peak time of the fire gradually delayed as the fire moved south. Our inventory shows that the DMB in southeast Australia during the study period was about 146 Tg, with major burned regions distributed along the Great Dividing Range, with December 2019 being the main burning period. The total DMB we calculated is 0.5–3.1 times that reported by the GFAS (Global Fire Assimilation System) and 1.5 to 4 times lower than that obtained using the traditional “Burned Area Based Method (FINN)”. We believe that the GFAS may underestimate the results by ignoring a large number of small fires, and that the excessive combustion rate used in the FINN may be a source of overestimation. Therefore, we conclude that the combination of high-temporal-resolution and high-spatial-resolution satellites can improve FRE estimation and may also allow further verification of biomass combustion estimates from different inventories, which are far better approaches for fire emission estimation.

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.