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This study evaluates the effectiveness of per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data in New York City. The combined approach improved classification accuracy for specific urban classes, such as white roofs and vegetation, which were initially low in accuracy when using per-pixel methods alone. The findings suggest that integrating both classification techniques can enhance urban mapping and provide valuable data for urban planning and environm

Author

scribd.bazooka518

Language

EN