Authors : Moses Tangwam

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/rud6rzat

Scribd : https://tinyurl.com/w4sw32nc

DOI : https://doi.org/10.38124/ijisrt/25dec1032

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Abstract : Object-based image analysis (OBIA) can improve high-resolution land-cover mapping by grouping pixels into meaningful image objects before classification, thereby reducing the salt-and-pepper noise common in pixel-based methods. This paper presents a reproducible OBIA workflow that integrates mean-shift image segmentation in ArcGIS Pro, rasterization of training polygons and segment identifiers, and supervised machine-learning classification using a Random Forest model implemented in Python (GDAL, NumPy, and scikit-learn). Training labels were transferred to segmented objects using a majority-label rule, while object-level features were computed as mean spectral values from a three-band (RGB) image. The workflow produces a classified raster map with spatially coherent objects, suitable for exploratory land- cover mapping in an urban park. It provides a foundation for adding richer features and formal accuracy assessment in future work.

Keywords : Object-Based Image Analysis; Segmentation; Mean Shift; Random Forest; scikit-learn; ArcGIS Pro; Supervised Classification; Raster Processing.

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