Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied.

Ship fire may result in significant damage to its structure and large economic loss. Hence, the prompt identification of fires is essential in order to provide prompt reactions and effective mitigation strategies. However, conventional detection systems exhibit limited efficacy and accuracy in detecting targets, which has been mostly attributed to limitations imposed by distance constraints and the motion of ships. Although the development of deep learning algorithms provides a potential solution, the computational complexity of ship fire detection algorithm pose significant challenges. To solve this, this paper proposes a lightweight ship fire detection algorithm based on YOLOv8n. Initially, a dataset, including more than 4000 unduplicated images and their labels, is established before training. In order to ensure the performance of algorithms, both fire inside ship rooms and also fire on board are considered. Then after tests, YOLOv8n is selected as the model with the best performance and fastest speed from among several advanced object detection algorithms. GhostnetV2-C2F is then inserted in the backbone of the algorithm for long-range attention with inexpensive operation. In addition, spatial and channel reconstruction convolution (SCConv) is used to reduce redundant features with significantly lower complexity and computational costs for real-time ship fire detection. For the neck part, omni-dimensional dynamic convolution is used for the multi-dimensional attention mechanism, which also lowers the parameters. After these improvements, a lighter and more accurate YOLOv8n algorithm, called Ship-Fire Net, was proposed. The proposed method exceeds 0.93, both in precision and recall for fire and smoke detection in ships. In addition, the mAP@0.5 reaches about 0.9. Despite the improvement in accuracy, Ship-Fire Net also has fewer parameters and lower FLOPs compared to the original, which accelerates its detection speed. The FPS of Ship-Fire Net also reaches 286, which is helpful for real-time ship fire monitoring.

Understanding the spatiotemporal distribution of land surface temperature (LST) is crucial for managing urban thermal environments and mitigating urban heat island (UHI) effects. This study addresses the challenge of quantifying the complex interactions among natural and anthropogenic factors driving LST variations, while leveraging advanced modeling techniques to predict future thermal risks in rapidly urbanizing regions. By analyzing the evolution of LST in Jinan city, China, from 2002 to 2022, and forecasts future trends using advanced spatial analysis and predictive modeling techniques. Directional shifts in LST were quantified using the quadrant azimuth method and the standard deviation ellipse method, both of which analyze spatial distribution and dispersion. To identify the key drivers of LST variations, 14 socioeconomic and environmental factors were assessed using the optimal parameter-based geographical detector (OPGD) model, which effectively handles spatial heterogeneity. Key findings include: (1) a significant northward shift in the LST centroid and a 26.64% expansion in high-temperature areas, with noticeable cooling effects in the city center. (2) A nonlinear relationship between LST and socioeconomic factors, particularly GDP, where cooling effects were observed when GDP exceeded 10,000 yuan/km². (3) Synergistic interactions, especially between topographic factors (such as the Digital Elevation Model, DEM) and land-use indices (e.g., normalized difference built-up index, NDBI; normalized difference vegetation index, NDVI), were found to significantly influence LST variations. (4) The Oscillating Sequence Grey Model (OSGM), optimized for handling oscillating data sequences, demonstrated superior predictive accuracy, projecting a 20.72% increase in extreme high-temperature zones and a 40.61% reduction in moderate-high-temperature zones by 2047. These findings offer actionable strategies for urban planning and climate adaptation, aiming to mitigate thermal risks and inform future policies for urban sustainability and resilience. This research underscores the importance of integrating spatial and predictive analyses to inform urban planning and climate adaptation strategies, contributing to the mitigation of thermal risks and the development of sustainable urban policies.

Urban heat and extreme-temperature risks increasingly constrain sustainable planning in semi-arid grassland towns. Taking Xi Ujimqin Banner as a case study, this research develops a remote-sensing-informed diagnosis-to-allocation framework for heat-resilient spatial planning. The framework first uses remote-sensing-derived land surface temperature (LST) and multi-source spatial indicators to identify model-explained associations between LST and ecological, morphological, land-use, accessibility, and socioeconomic variables. A CatBoost model with SHAP, PDP, and ICE interpretation was applied to explain dominant LST-related patterns. Spatial units were then classified by integrating LST intensity, sky view factor (SVF)-related spatial openness, land-use structure, ecological sensitivity, and economic output characteristics. Finally, a Quasi-oppositional Learning and Levy-flight enhanced Cheetah Optimization Algorithm (QLA-COA)-based constrained optimization model was used to generate alternative allocation schemes. In the framework, LST reduction and land-development economic benefit were treated as the two primary planning objectives, while SVF-related openness was incorporated as an auxiliary spatial form and radiative geometry indicator rather than as a direct temperature-control objective. Results show that CatBoost achieved the best LST prediction performance (R² = 0.834, RMSE = 1.762 °C, MAE = 1.371 °C). Cooling-priority, openness-priority, economic-priority, balanced-development, and ecological-restricted units accounted for 18.7%, 15.8%, 12.9%, 26.9%, and 25.7% of all units, respectively. The balanced-development scheme reduced mean LST by 1.30 °C while maintaining SVF-related spatial openness and improving economic benefits. This study provides a practical decision-support framework for climate-adaptive spatial governance in semi-arid grassland regions by linking LST diagnosis, spatial response translation, ecological constraints, auxiliary openness evaluation, and allocation-scheme comparison. The source code and processed spatial unit dataset used for model training, SHAP interpretation, and PDP/ICE visualization are publicly available.