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Involve handling vast amounts of geospatial imagery data efficiently and extracting meaningful insights from it. This process requires robust infrastructure, advanced algorithms, and scalable solutions to manage, process, and analyze imagery seamlessly. By leveraging cloud computing, distributed processing, and parallel computing techniques, organizations can effectively handle large volumes of imagery data. Automated feature extraction, object detection, change detection, and classification algorithms play a crucial role in analyzing imagery at scale. Additionally, the integration of machine learning and deep learning models enables the extraction of valuable information from imagery, facilitating tasks such as land cover mapping, urban growth monitoring, disaster response, and environmental monitoring. In this session, let’s explore effective imagery management and analysis at scale for empowering organizations to make informed decisions, derive actionable insights, and drive innovation in various domains such as agriculture, urban planning, natural resource management, and disaster management. Recorded February 25, 2025 #EsriUAEUC #ArcGIS #ArcGISNotebook #ArcGISPro #ArcGISEnterprise