Design and Analysis of Environmental Maps for Future Scenarios Using Artificial Intelligence Spatial Applications in Iraq

Abstract

Artificial intelligence models have demonstrated accuracy in classifying land cover and predicting future environmental changes, establishing the optimal approach for creating environmental maps. Our research focuses primarily on the design and analysis of environmental maps and spatial applications in Iraq for future scenarios (optimistic, moderate, and pessimistic), which provide visual tools for decision-makers to evaluate climate change mitigation strategies. The research showed that unplanned urban expansion and climate change are the main drivers of vegetation cover degradation and changing environmental conditions; these risks were modeled using artificial intelligence. To produce future maps (such as a sustainable development scenario versus a degradation scenario), the research relied on the model-based methodology (lovart.ai/canvas) and integrated satellite imagery data (Landsat and Sentinel) to train algorithms to build predictive models of Iraq’s environmental reality, using current climate data to feed machine learning algorithms. The focus was on maps of (water resources, climatic factors, land use, environmental issues, biodiversity, protected areas, environmental scenarios, future sustainable development, the Grand Faw Port, and a 3D simulation of the Masad Dam) for each of the provinces (Anbar, Salah al-Din, Basra, and Kirkuk), the results highlighted challenges related to a lack of ground truth data in some areas, along with realistic and future environmental maps to address environmental issues and support spatial environmental decision-making.