Land-use change detection between the master plan and the current state by using GIS and RS the case study of the city Al-Hay

Abstract

The expansions of cities and continuous urbanization have prepared comprehensive urban plans necessary. The important thing about these plans is to determine their success, efficiency, and feasibility. The expansion of the city comes because of the population increase or investment. When the expansion of cities precedes the updating of its master plans and the development of alternative plans to accommodate these expansions, it is normal that finds irregularities in use and differences between the master plan and real land use. Therefore, the main purpose of this paper is to examine the extent of illegal changes in the current situation in comparison with master plans. Also, the efficiency of artificial neural network models is evaluated to predict illegal land-use changes. The old master plan (1990-2010) and the new one (2010-2030) was used with two high-resolution satellite images dated 2010 and 2018. To determine changes between the status quo and the uses defined in the master plan, the required information tables were completed by field visit with questioner made for Al-hay City. Neural network models were used to evaluate the survey, data was divided into two parts training and testing then a neural network model was performed on them after testing the results using the neural network model, the results present that the accuracy of about 85% for the old master plan and pass 91% for the updated master plan. The largest percentage of differences between the real use and master plan for the year 2010 was in (Religious use, Parking, Gas stations, then government use, green areas, educational use, and residential), but note that the largest percentage of differences between the real use and master plan for the year 2018 were in (Religious use, Athletic use, Green areas, then commercial and governmental uses).