An integrated semiautomated and transferable data driven approach for urban texture mapping

Urbanization has significantly increased over the past decades, making monitoring of urban growth and urban texture essential for urban planning and sustainable development. In this context, the classification of different urban textures has gained importance, leveraging advancemnts insatellite image processing and methods such as machine learning and object-based image analysis (OBIA), as well as their integration. The present study aims to apply and evaluate different object-based methods to map urban texture in different part of Tabriz city in Iran. To this end, five area with distinct urban texture patterns were selected and analyzed using OBIA’s spectral and spatial features. A semiautomated OBIA approach was developed and applied to map urban textures, and its robustness and efficiency was examinedOur analysis indicated that combining the average, shape, and gray level co-occurrence matrix methods enhances the ability to identify objects in urban environments. The results highlight the high potential of OBIA algorithms and features in detecting and classifying urban areas. This study provides valuable insights for urban planners, offering a useful tool for informed decision-making in future urban development.