AI Insight
This study used machine learning and satellite imagery to analyze urban expansion in Gambella Town, Ethiopia from 2005 to 2025, revealing a dramatic increase in built-up area from 93 hectares (2.0%) to 1,956 hectares (41.3%). The expansion occurred primarily at the expense of grassland and bush areas, which declined from 71.1% to 13.7% of the study area, while agricultural land also increased significantly. Random Forest classification achieved 89-90% accuracy in identifying six land use categories across the three time periods.
Why it matters
The findings provide evidence-based insights for sustainable urban planning in rapidly growing Ethiopian towns, identifying key drivers like population growth and rural-urban migration that are contributing to vegetation loss and strain on urban infrastructure. The methodology demonstrates how machine learning can be effectively integrated with geospatial analysis to monitor and predict urban land use changes in data-scarce regions.
Understand the Science
by Kanenus Fufa Dararo, Goanar Jal Dup, Weyesa Merga Chemeda, Shankar Karuppannan
Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.