Heavy-duty trucks serve as the backbone of the supply chain and have a tremendous effect on the economy. However, they severely impact the environment and public health. This study presents a novel truck detection framework by combining satellite imagery with Geographic Information System (GIS)-based OpenStreetMap data to capture the distribution of heavy-duty trucks and shipping containers in both on-road and off-road locations with extensive spatial coverage. The framework involves modifying the CenterNet detection algorithm to detect randomly oriented trucks in satellite images and enhancing the model through ensembling with Mask RCNN, a segmentation-based algorithm. GIS information refines and improves the model's prediction results. Applied to part of Southern California, including the Port of Los Angeles and Long Beach, the framework helps assess the environmental impact of heavy-duty trucks in port-adjacent communities and understand truck density patterns along major freight corridors. This research has implications for policy, practice, and future research.

Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and are difficult to generalize to unseen classes. Few-shot learning has the potential to address these challenges by learning new classes from only a few labeled examples. In this work, we propose a new framework for few-shot medical image segmentation based on prototypical networks. Our innovation lies in the design of two key modules: 1) a context relation encoder (CRE) that uses correlation to capture local relation features between foreground and background regions; and 2) a recurrent mask refinement module that repeatedly uses the CRE and a prototypical network to recapture the change of context relationship and refine the segmentation mask iteratively. Experiments on two abdomen CT datasets and an abdomen MRI dataset show the proposed method obtains substantial improvement over the state-of-the-art methods by an average of 16.32%, 8.45% and 6.24% in terms of DSC, respectively. Code is publicly available1.

Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achieving clinically applicable image segmentation performance on CT or MRI images. State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution. To fit a 3D CNN model on CT or MRI images on commodity GPUs, one usually has to either downsample input images or use cropped local regions as inputs, which limits the utility of 3D models for multi-organ segmentation. In this work, we propose a new framework for combining 3D and 2D models, in which the segmentation is realized through high-resolution 2D convolutions, but guided by spatial contextual information extracted from a low-resolution 3D model. We implement a self-attention mechanism to control which 3D features should be used to guide 2D segmentation. Our model is light on memory usage but fully equipped to take 3D contextual information into account. Experiments on multiple organ segmentation datasets demonstrate that by taking advantage of both 2D and 3D models, our method is consistently outperforms existing 2D and 3D models in organ segmentation accuracy, while being able to directly take raw whole-volume image data as inputs.