Interface GIS using computer vision ai/ml for supply chain management
Creators: name: Kim, Minseok
Publisher: name: No Publisher Supplied
Year: 2024

description: In recent years, pandemics, disasters, and wars have caused significant supply chaindisruptions, underscoring the need for responsive supply chain with agility. Even thoughimproving supply chain visibility is crucial with advanced data analytics, in this data-drivenera, gathering reliable and affordable data poses a considerable challenge. This is particularlytrue in developing countries with inadequate infrastructure and data collection institutions.In this thesis, we aim to address this issue by leveraging geospatial data withcutting-edge computer vision AI/ML to identify building infrastructure and track transportationobjects, an innovative approach unattempted in supply chain management.In essay 1, we introduce a novel framework that merges Computer Vision AI/ML withdata Mining on geospatial data, aiming to accurately identify and classify various buildingstructures, such as warehouses, factories, stores, residential buildings, and apartments,including both row and high-rise types. It further applies this methodology to simplifylabeling through multi-class segmentation from a binary building mask, focusing on threedistinct urban features in South Korea. Our model demonstrates great performance in identifyingbuildings from satellite and aerial images, achieving over 93%-pixel accuracy and90% accuracy in type classification. Additionally, we apply shadow overlapping simulationto estimate the height of the buildings and generate 3D views of the buildings. Thisapproach improves waste management and the distribution of electric vehicle (EV) chargiiing stations, providing economic insights for the development of developing regions likeCambodia.. In Essay 2, we develop a model that accurately identifies and counts cargo containersin and near port facilities, utilizing advanced computer vision AI and machine learningtechniques, which incorporate shadow extraction and simulation on satellite imagery. Themodel is tested in five different ports in the United States, Spain, and the United Kingdom,showing a robust accuracy of 88-96% and this approach enables the monitoring the port andnear port facilities which can be the clue for alerting of port congestion levels, assisting inthe management of global supply chain disruptions.In Essay 3, we focus on accurately detecting the operational status of airplanes at globalairports by combining GIS with machine learning algorithms. Our model, tested on the top23 busiest airports worldwide, achieves 96% accuracy in detecting airplanes and classifiesthem into categories such as passenger, moving, air cargo, aviation, parking, and maintenancewith 93% accuracy using satellite imagery and GIS-enhanced spatial analysis.Thisresearch has practical applications in airport control, facilitating real-time airplane monitoringand traffic flow optimization for traffic and congestion control.We believe that pioneering the integration of computer vision AI and geospatial data isrevolutionizing supply chain management by leveraging AI’s potential to enhance visibilityand responsiveness. Future efforts should focus on keeping pace with the rapid evolutionof AI/ML models to improve accuracy and on leveraging the increasing affordability ofgeospatial data for broader research applications. Our methodology will ultimately enableus to track transit trends and facilitate responsive disaster relief through remote damageassessment.