The detection of Agricultural Land Changes Using Deep learning and open source images

Land cover assessment is a significant research area in GIS and remote sensing, aiding decision-makers in understanding land use changes' underlying forces and enabling effective actions. In general, Iraqi cities are experiencing severe degradation of agricultural lands due to population growth and residential development, impacting socio-economic and environmental quality. In addition, the deriving forces of transforming the lands from agricultural to other land cover types are not well understood. Research is needed to map and assess agricultural lands for better economic and environmental solutions. The study uses ANN-CA integration to predict agricultural land changes in Babil province, central Iraq. The CNN model achieved the highest accuracy, with a total land cover transformation of 2143.1 square kilometres between 2000 and 2020. the overall accuracy was 0.95, 0.93, and 0.90 based on images captured in 2020, 2000, 2010) respectively. This methodology is considered an efficient tool for monitoring agricultural lands and developing development plans in Iraq.