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When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model that strategically combines the strengths of Transformers and Convolutional Neural Networks (CNNs). AerialFormer features a CNN Stem module integrated to preserve low-level and high-resolution features, enhancing the model’s capability to process details of aerial imagery. The proposed AerialFormer is designed with a hierarchical structure, in which a Transformer encoder generates multi-scale features and a multi-dilated CNN (MDC) decoder aggregates the information from the multi-scale inputs. As a result, information is taken into account in both local and global contexts, so that powerful representations and high-resolution segmentation can be achieved. The proposed AerialFormer was benchmarked on three benchmark datasets, including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that the proposed AerialFormer remarkably outperforms state-of-the-art methods. 

One essential task in practice is to quantify and improve the reliability of an infrastructure network in terms of the connectivity of network components (i.e., all-terminal reliability). However, as the number of edges and nodes in the network increases, computing the all-terminal network reliability using exact algorithms becomes prohibitive. This is extremely burdensome in network designs requiring repeated computations. In this paper, we propose a novel machine learning-based framework for evaluating and improving all-terminal network reliability using Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL). With the help of DNNs and Stochastic Variational Inference (SVI), we can effectively compute the all-terminal reliability for different network configurations in DRL. Furthermore, the Bayesian nature of the proposed SVI+DNN model allows for quantifying the estimation uncertainty while enforcing regularization and reducing overfitting. Our numerical experiment and case study show that the proposed framework provides an effective tool for infrastructure network reliability improvement.