Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

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Abstract. The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

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