Vanilla Transformers focus on semantic relevance between mid‐ to high‐level features and are not good at extracting smoke features, as they overlook subtle changes in low‐level features like color, transparency, and texture, which are essential for smoke recognition. To address this, we propose the cross contrast patch embedding (CCPE) module based on the Swin Transformer. This module leverages multiscale spatial contrast information in both vertical and horizontal directions to enhance the network’s discrimination of underlying details. By combining cross contrast with the transformer, we exploit the advantages of the transformer in the global receptive field and context modeling while compensating for its inability to capture very low‐level details, resulting in a more powerful backbone network tailored for smoke recognition tasks. In addition, we introduce the separable negative sampling mechanism (SNSM) to address supervision signal confusion during training and release the SKLFS‐WildFire test dataset, the largest real‐world wildfire test set to date, for systematic evaluation. Extensive testing and evaluation on the benchmark dataset FIgLib and the SKLFS‐WildFire test dataset show significant performance improvements of the proposed method over baseline detection models.

Wildfires pose a critical threat to human life, property safety, and ecological systems. Weather radar has demonstrated significant potential as an effective wildfire monitoring tool. However, as the dominant sources of radar backscattering signatures, the mechanisms of particle scattering are unclear. In this study, the microwave scattering characteristics of pyrometeors (lofted above wildfires that are composed of the byproducts of fuel combustion) were systematically investigated through integrated experimental measurements and numerical simulations. The complex permittivity of pyrometeors was measured across 1–18 GHz using the coaxial transmission/reflection method. The scattering properties of pyrometeors at microwave frequencies were determined using the discrete dipole approximation method. Results indicate that the scattering polarization characteristics of pyrometeors are regulated by particle size and aspect ratio within microwave frequencies. The magnitude ofF11(π) is positively correlated with particle aspect ratio under horizontal orientation. The backscattering cross section exhibits an inverse proportionality to incident wavelength across L–Ku-bands and a positive proportionality to particle size. C-, X-, and Ku-band radars show high sensitivity to pyrometeor morphology, necessitating precise shape modeling for accurate echo interpretation. Differential reflectivity ZDR exhibits nonmonotonic frequency dependence within the Mie scattering regime, confirming the joint influence of particle size and shape on polarimetric response. The effective reflectivity factor derived from this experiment falls within the range of observed measurements, supporting the feasibility of this study method in modeling the microwave scattering properties of pyrometeor particles.

Global illumination combines direct and indirect lighting to create realistic lighting effects, bringing virtual scenes closer to reality. Static global illumination is a crucial component of virtual scene rendering, leveraging precomputation and baking techniques to significantly reduce runtime computational costs. Unfortunately, many existing works prioritize visual quality by relying on extensive texture storage and massive pixel-level texture sampling, leading to large performance overhead. In this paper, we introduce an illumination reconstruction method that effectively reduces sampling in fragment shader and avoids additional render passes, making it well-suited for low-end platforms. To achieve high-quality global illumination with reduced memory usage, we adopt a spherical harmonics fitting approach for baking effective illumination information and propose an inverse probe distribution method that generates unique probe associations for each mesh. This association, which can be generated offline in the local space, ensures consistent lighting quality across all instances of the same mesh. As a consequence, our method delivers highly competitive lighting effects while using only approximately 5% of the memory required by mainstream industry techniques.

Highly frequent wildfires are observed every year in Yunnan province, China. This study investigated the smoke injection height in a wildfire event using multi-source remote sensing data in Yunnan. The wildfire event was identified and characterized by combining MODIS and Landsat-8 data. The smoke injection height of the event and the vertical profile of smoke aerosol were inferred from CALIPSO data. The results showed that although the burned areas were only 167.67 ha, the maximum smoke injection height of the wildfire event can reach approximately 4.63 km above sea level. In addition, by comparing the smoke injection height with that of other types of fire events, we found that the plumes of the wildfire event in Yunnan were the most likely candidates for injecting smoke into the free troposphere and producing smoke transport. This study has significant implications for understanding the impact of wildfire smoke plumes on air quality in Yunnan.