Ship Detection in Remote Sensing Imagery for Arbitrarily Oriented Object Detection

This research paper presents an innovative ship detection system tailored for applications like maritime surveillance and ecological monitoring. The study employs YOLOv8 and repurposed U-Net, two advanced deep learning models, to significantly enhance ship detection accuracy. Evaluation metrics include Mean Average Precision (mAP), processing speed, and overall accuracy. The research utilizes the "Airbus Ship Detection" dataset, featuring diverse remote sensing images, to assess the models' versatility in detecting ships with varying orientations and environmental contexts. Conventional ship detection faces challenges with arbitrary orientations, complex backgrounds, and obscured perspectives. Our approach incorporates YOLOv8 for real-time processing and U-Net for ship instance segmentation. Evaluation focuses on mAP, processing speed, and overall accuracy. The dataset is chosen for its diverse images, making it an ideal benchmark. Results demonstrate significant progress in ship detection. YOLOv8 achieves an 88% mAP, excelling in accurate and rapid ship detection. U Net, adapted for ship instance segmentation, attains an 89% mAP, improving boundary delineation and handling occlusions. This research enhances maritime surveillance, disaster response, and ecological monitoring, exemplifying the potential of deep learning models in ship detection.

A Predictive and Optimization Approach for Enhanced Urban Mobility Using Spatiotemporal Data

In modern urban centers, effective transportation management poses a significant challenge, with traffic jams and inconsistent travel durations greatly affecting commuters and logistics operations. This study introduces a novel method for enhancing urban mobility by combining machine learning algorithms with live traffic information. We developed predictive models for journey time and congestion analysis using data from New York City's yellow taxi trips. The research employed a spatiotemporal analysis framework to identify traffic trends and implemented real-time route optimization using the GraphHopper API. This system determines the most efficient paths based on current conditions, adapting to changes in traffic flow. The methodology utilizes Spark MLlib for predictive modeling and Spark Streaming for processing data in real-time. By integrating historical data analysis with current traffic inputs, our system shows notable enhancements in both travel time forecasts and route optimization, demonstrating its potential for widespread application in major urban areas. This research contributes to ongoing efforts aimed at reducing urban congestion and improving transportation efficiency through advanced data-driven methods.