Fine-grained wildfire spread prediction is crucial for enhancing emergency response efficacy and decision-making precision. However, existing research predominantly focuses on coarse spatiotemporal scales and relies on low-resolution satellite data, capturing only macroscopic fire states while fundamentally constraining high-precision localized fire dynamics modeling capabilities. To bridge this gap, we present FireSentry, a provincial-scale multi-modal wildfire dataset characterized by sub-meter spatial and sub-second temporal resolution. Collected using synchronized UAV platforms, FireSentry provides visible and infrared video streams, in-situ environmental measurements, and manually validated fire masks. Building on FireSentry, we establish a comprehensive benchmark encompassing physics-based, data-driven, and generative models, revealing the limitations of existing mask-only approaches. Our analysis proposes FiReDiff, a novel dual-modality paradigm that first predicts future video sequences in the infrared modality, and then precisely segments fire masks in the mask modality based on the generated dynamics. FiReDiff achieves state-of-the-art performance, with video quality gains of 39.2% in PSNR, 36.1% in SSIM, 50.0% in LPIPS, 29.4% in FVD, and mask accuracy gains of 3.3% in AUPRC, 59.1% in F1 score, 42.9% in IoU, and 62.5% in MSE when applied to generative models. The FireSentry benchmark dataset and FiReDiff paradigm collectively advance fine-grained wildfire forecasting and dynamic disaster simulation. The processed benchmark dataset is publicly available at: https://github.com/Munan222/FireSentry-Benchmark-Dataset.

Fine-grained air pollution forecasting is crucial for urban management and the development of healthy buildings. Deploying portable sensors on mobile platforms such as cars and buses offers a low-cost, easy-to-maintain, and wide-coverage data collection solution. However, due to the random and uncontrollable movement patterns of these non-dedicated mobile platforms, the resulting sensor data are often incomplete and temporally inconsistent. By exploring potential training patterns in the reverse process of diffusion models, we proposeSpatio-TemporalPhysics-InformedDiffusion Models (STeP-Diff). STeP-Diff leverages DeepONet to model the spatial sequence of measurements along with a PDE-informed diffusion model to forecast the spatio-temporal field from incomplete and time-varying data. Through a PDE-constrained regularization framework, the denoising process asymptotically converges to the convection-diffusion dynamics, ensuring that predictions are both grounded in real-world measurements and aligned with the fundamental physics governing pollution dispersion. To assess the performance of the system, we deployed 59 self-designed portable sensing devices in two cities, operating for 14 days to collect air pollution data. Compared to the second-best performing algorithm, our model achieved improvements of up to 89.12% in MAE, 82.30% in RMSE, and 25.00% in MAPE, with extensive evaluations demonstrating that STeP-Diff effectively captures the spatio-temporal dependencies in air pollution fields.

Mobile air pollution sensing methods have emerged to collect air quality data with improved spatial and temporal resolutions. However, existing methodologies struggle to effectively process spatially mixed gas samples due to the highly dynamic fluctuations experienced by sensors, resulting in significant measurement deviations. We identify an opportunity to address this issue by exploring potential patterns within sensor measurements. To this end, we propose CatUA, a novel city-scale fine-grained air quality estimation system designed to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model specifically aimed at discerning mixed gas concentrations from sensor data. Second, we implement a Prompt-informed Training Strategy that leverages extensive unlabeled and minimal labeled city-scale data to enhance the performance of CatUA. Notably, the Auto-Prompt mechanism allows CatUA to conveniently acquire new knowledge tailored to specific downstream tasks. To ensure the practicality of CatUA, we have invested considerable effort in developing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered city-scale air quality data for over 1,200 hours. Experiments conducted on the collected data demonstrate that CatUA reduces sensing errors by 96.9% with a latency of only 44.9ms, outperforming the state-of-the-art baseline by 42.6%.