Engineering and Technology Journal Vol. 38, Part A (2020), No. 04, Pages 510-514 Engineering and Technology Journal engtechjournal.org Journal homepage: Publishing rights belongs to University of Technology’s Press, Baghdad, Iraq. 510 Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping Tay H. Shihab a* , Amjed N. Al-Hameedawi b , Ammar M. Hamza c a Civil Engineering Department, University of Technology, Baghdad, Iraq. Email: tay.hatem333@gmail.com b Civil Engineering Department, University of Technology, Baghdad, Iraq. c Civil Engineering Department, University of Technology, Baghdad, Iraq. *Corresponding author. Submitted: 26/06/2019 Accepted: 05/09/2019 Published: 25/04/2020 KEYWORDS ABSTRACT Artificial neural network, LULC Mapping, Random forest. In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively. How to cite this article: T. H. Shihab, A.N. Al-Hameedawi and A.M. Hamza, “Random forest (RF) and artificial neural network (ANN) algorithms for LULC Mapping,” Engineering and Technology Journal, Vol. 38, Part A, No. 04, pp. 510-514, 2020. DOI: https://doi.org/10.30684/etj.v38i4A.399 1. Introduction Nowadays, with progress in remote sensing gathering technology and the increased call for remote sensing programs, high spatial decisions far off sensing, statistics are regularly turning into greater great [1]. The availability and accessibility of significant amounts of high-decision far off sensing information have created a venture for faraway sensing image class. As a result, Artificial Neural Network and