Deep Learning-Based Prediction of Urban Area Expansion

Abstract

Multi-temporal data can be useful in many research areas. Multi-temporal data provides us with high-resolution satellite imagery. This high-resolution data can be used to understand change detection in a specific area or the layout of that complete area. To use multi-temporal data, especially custom datasets for deep learning models is real headache. Moreover, Multi- temporal data can be very useful with Artificial Intelligence to develop models to tackle different problems like wildfire detection, traffic flow detection, etc. This thesis focuses on two problems 1st one is how can custom multi-temporal dataset which consists of a small sample be used to develop a deep learning model for semantic segmentation purpose of given custom dataset and 2nd problem is related to Urban expansion. The problem of urban expansion is a major issue all over the world, especially in the countries of Africa. Urban expansion has a direct impact on both economic growth and climate change. In this era Machine learning and Computer Vision techniques will provide a vital role to create a model which will help to tackle this problem using multi-temporal data. This thesis methodology is to use custom satellite images data set of a specific area and create a deep learning segmentation model to do segmentation and prediction of different areas in satellite images especially, the urban part. This research focuses on the RGB multi-temporal data set of Dakar, Senegal which is one of the Seaports on the Western Coast of Africa. This thesis methodology is divided into two separate parts. 1st part is about segmentation, an experiment was conducted using with simple Multi U-Net architecture and it achieved more than 85% accuracy on the validation dataset. After these predictions were made using random images from the test dataset. 2nd part focused on the urban expansion problem and trained a Regression model using an Artificial Neural Network which only got a 12% MSE value with 97.25% accuracy and predicts urban expansion on basis of pixel values from remote sensing data using population data from the year 2022 to 2031.