This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

Aircraft condensation trails (contrails), formed in ice supersaturation regions, are human-induced clouds with significant climate warming effects. Mitigating their climate impact through feasible flight rerouting requires precise contrail detection, tracking, and prediction from satellite imagery. Given the potential divergent feature distributions in the frequency domain of comparable contrail observations, this study proposes to integrate frequency-oriented learning and regularization into encoder–decoder-based semantic segmentation models. It includes a lightweight separable frequency-aware block to capture complementary patterns in feature extraction and an auxiliary regularization based on frequency-domain discrepancy for active samples in the loss function. Evaluated on two human-labeled contrail imagery datasets (GOES-16 and Landsat-8), our approach achieves consistent improvements over the state-of-the-art methods across a suite of metrics with a minimal decrease in inference speed. Ablation studies confirm the effectiveness of incorporating frequency-domain knowledge into contrail detection. This study also evaluates contrail detection performance across contrail coverage variations, significantly enhancing the detection of short and fragmented contrail segments. The generalization of our method across two datasets with varying resolutions and label granularity suggests its potential for real-world deployment in aviation-induced climate impact mitigation. Furthermore, the benchmarks established in this study provide a foundation for future research in the field.