Assessment of Urban Development Expansion Using Object-Based Image Classification Technique. A Case Study of Asokoro City, Federal Capital Territory, Abuja, Nigeria.

Abstract

Urban development expansion is critical to urban planning and management, particularly in rapidly growing cities. This study uses object-based image classification techniques to assess the urban development expansion of Asokoro, a prominent city in the Federal C capital Territory, Abuja. Satellite multi-spectral image data was employed to capture spatial changes over time, providing valuable insights into the dynamics of urban growth. The methodology involves collecting multi-temporal satellite image data covering various periods of three epochs (2003, 2013 and 2023) to analyses the trend expansion patterns of the study area. The Google image was pre-processed using a dehazing and shadow extraction algorithm. The segmentation process was carefully carried out, after which the features were extracted. Object-based image classification technique was utilized to accurately delineate urban features and monitor dynamics in land use and land cover of the study area. This approach allows for a more detailed and precise analysis than pixel-based classification methods. The outcome recorded about 14.42% of built-up areas in the year 2003. In the year 2013 and 2023, it recorded an increasing trend of about 5.21% and 7.03% respectively. Simple least square regression analyses technique was used to projection the built-up areas to the next ten years (2033). At 95% significant level, the built-up area is expected to record an increasing trend of about 7.24% which translate to be about 34.34% of the total area of Asokoro. The results reveal significant urban development expansion within the study area, characterized by increased built-up areas, infrastructure development, and changes in land use patterns. The findings provide valuable information for urban planners and policymakers to understand the spatial dynamics of Asokoro's growth and formulate sustainable development strategies.