GeoAI: AI-Driven model for Land Assessment and Risk Prediction

Abstract

Land suitability assessment is one of the key aspects of modern urban development. If there is any mistake in assessing land suitability, environmental hazards, and financial losses may occur. Land suitability assessment is a necessity in modern urban development, taking into account the rapid growth of the urban population and the increase in infrastructural development. Though there is a huge amount of geospatial information available, land suitability assessment is a complex and time-consuming process, considering the fragmented geospatial information gathered from various sources. To avoid the complexities in the land suitability assessment process, which involves gathering geospatial information from various sources, a digital solution is proposed for efficient collection of geospatial information from various sources using a single platform. The solution uses smart algorithms for assessing land suitability based on environmental conditions. This solution provides safer construction practices, which helps minimize environmental risks. Moreover, economic losses are avoided by ensuring efficient use of land. This solution improves efficiency, accuracy, and reliability in land suitability assessments.

Introduction

Urbanization and population growth have increased the demand for land for residential, commercial, and agricultural use, making land suitability assessment a critical but challenging task. Poor planning and incomplete evaluation of key environmental and geographical factors—such as soil strength, rainfall patterns, pollution levels, flood history, and land use—can lead to unsafe construction, economic losses, and long-term environmental damage. Traditional methods like GIS-based analysis are often manual, complex, expensive, and not easily accessible, which creates a gap between technical analysis and real-world decision-making.

To address these limitations, the proposed GeoAI Land Suitability Analyzer integrates Machine Learning (especially Random Forest), Geographic Information Systems (GIS), and real-time environmental data to generate a Land Suitability Score. This score classifies land into Suitable, Moderate, or Unsuitable categories, helping users make safer and more informed decisions.

Existing studies show that machine learning models like Random Forest and Extra Tree Classifier generally perform better than traditional methods such as SVM and rule-based GIS approaches. However, most existing systems rely on static datasets, limited geographic regions, or lack real-time adaptability. Tools like PyLUSAT and MCDA-based methods improve structured analysis but do not fully support dynamic, predictive land assessment.

Conclusion

Sustainable land suitability platform is not only how land is being evaluated, but leads to sustainable development which gives which may not give exactly accurate but gives good data driven insights helping people to make safer and more environmentally friendly choices and how to use a particular land over a human activity. This can be improved in terms of scalability with new technologies on time being counted with a variety of environmental settings and can be directly applied to real world settings.

References

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Copyright

Copyright © 2026 Dr. G Naga Chandrika, Adepu Vaishnavi, Chinni Jyothika, Maganti Pranathi, Harsha Vardhan Botlagunta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.