Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data

Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification

“…According to several studies, different classifiers perform differently when mapping different LULC classes for the same data and study year ( Abdi 2020 ). In the present research, this change is also detected in the outcomes of machine learning models for the LULC mapping of the study area ( Tariq et al 2023b ).…”

“…Therefore, the monitoring of water quantity and quality ( Tariq et al , 2022( Tariq et al , 2023a; land management ( Asif et al 2023 ;Zheng et al 2023 ); hazards; and risk assessment all needs a comprehensive LULC map as a dynamic element. The high-resolution data processed by innovative and advanced geospatial techniques ) have a significant position in managing the area's land use ( Mary et al 2023 ;Tariq et al 2023b ).…”

Land subsidence analysis using synthetic aperture radar data

“…It includes a bay, which is up to 240 m wide, separating the Western bay called “Paddi Zirr,” which is up to 9.1 m deep, and the Eastern bay called Demi Zirr, which is the deep-water bay, providing the land to Gwadar port [ 45 ]. The Gwadar foreland consists of two of the highest peaks, known as Koh-i-Batel, which is up to 137 m high, spreading up to 11 km, and Koh-i-Mehdi, 340 m tall [ 3 , 10 , 40 , 42 , 46 ].…”

“…InSAR processes have been the most integrated and functional tool in measuring the displacement of the earth’s surface on a millimeter scale. It is used worldwide and has widespread utilization in measuring displacement related to natural hazards, for example, earthquake, volcanic activity, tsunami, and landslides [ 38 , 41 , 42 ]. The process involves a high density of measuring points over remote, unapproachable, and risky areas.…”

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

“…Various plant classification techniques are currently available; thus, selecting the most appropriate method depends on the site-specific characteristics. Traditional spatial-based classification techniques such as Random Forest (RF) [11,12] and Support Vector Machine (SVM) [13] have been broadly exploited in numerous studies where comparisons with other classifications have been conducted [14][15][16]. However, recent advancements in machine learning (ML) have led to the development of deep learning classification techniques for plant species classification, such as Convolutional Neural Networks (CNN) [17,18], Long Short-Term Memory Networks (LSTMs) [19,20], Recurrent Neural Networks (RNNs) [21,22] and Multilayer Perceptrons (MLPs) [23].…”