Water-body Segmentation in Heterogeneous Hydrodynamic and Morphodynamic Structured Coastal Areas by Machine Learning

Abstract

Coastal areas constitute the most important part of the world when considered in terms of their socio-economic and natural values. Measuring and monitoring the coastal areas accurately is an important issue for coastal management. Compared to ground-based studies, remote sensing applications enriched with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) provide significant benefits in terms of cost, time, and size of the study area. Within the scope of this study, Sentinel-2 images for five coastal areas located in Turkey with different morphological and hydrodynamic properties were classified as land and water-bodies using SVM and RF algorithms. Water-body segmentation results of the SVM and RF classification for the different band combinations of Sentinel-2 images have been compared. The reasons affecting the results of the accuracy analysis were examined in accordance with the geography of each area. Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.

Estimation of Urban Area Change in Eskişehir Province Using Remote Sensing Data and Machine Learning Algorithms

“…The land use classes in each data set obtained were determined and their areas were calculated. In the second stage of the study, urban area change was modeled with machine learning algorithms by using land use changes (Çelik and Gazioğlu, 2022;Gümüşçü, et al, 2023). One of the methods used for this purpose in the study is the Sequential Minimum Optimization Regressor (SMOReg) method.…”