Optimized Artificial Neural Network for the Classification of Urban Environment Comfort using Landsat-8 Remote Sensing Data in Greater Jakarta Area, Indonesia

ABSTRACT
The development of computer vision technology as a type of artificial intelligence is increasing rapidly in
various fields. This method uses deep learning methods based on artificial neural networks, a well
performed algorithm in multi-parameter analysis. One of the development of computer vision models and
algorithms is for a thematic digital image classification, such as environmental analysis. Remote sensing
based digital image classification is one of the reliable tools for environmental quality analysis. This study
aims to perform neural network optimization for the analysis of the urban environment comfort based on
satellite data. The input data used are 4 types of geobiophysical indexes as urban environmental comfort
parameters derived from cloud-free annual mosaics Landsat-8 remote sensing satellite data. The results
obtained in this study indicate that the 1 hidden layer neural network architecture with 16 neurons for the
classification of urban environmental comfort and 10 other land cover classes is quite good. The result of
the classification using this optimized artificial neural network shows that the distribution of classes is very
uncomfortable which dominates the Greater Jakarta area and its surroundings. For other classes in the
study area, some are uncomfortable and rather comfortable. By using this method, we obtained a fast
classification training time of 18 seconds for 145 iterations to achieve an RMS Error of 0.01, and has a
fairly high classification accuracy overall 89% with a Kappa coefficient of 0.88, while the 2 hidden layer
neural network architecture does not succeed in achieving convergence.