Creation of the fourth-dimension map for urban areas in Baghdad city using remote sensing and GIS techniques

The primary objective of this study is to identify and analyze the transformation of urban districts in Baghdad over the course of three decades, spanning from 1993 to 2023. The images of Baghdad captured by the Landsat sensors Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) were sourced from the official website of the United States Geological Survey (USGS). After done preprocessing, a supervised classification by the Support Vector Machine (SVM) algorithm was used to classify the images into (LULC) groups. The study area is classified to five (LULC) categories, namely urban areas, soil, vegetation, water bodies, and wetlands. The accuracy assessment calculated for the classification and the results were 89%, 91%, 92%, and 90% for 1993, 2003, 2013, and 2023, respectively. the change detection results show that urban areas grow over the specified time intervals. there was an increase of 1.34%, 1.62%, 1.98%, and 4.54% in 1993-2003, 2003-2013, 2013-2023, and 1993-2023, respectively. Then used change detection layers to produce a fourth-dimension map.