Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

Floods have been a major cause of destruction, instigating fatalities and massive damage to
the infrastructure and overall economy of the affected country. Flood-related devastation …

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS
techniques have been successfully utilized to evaluate the chlorophyll content for the …

Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …

Remote sensing imagery (RSI) and point of interest (POI) are two complementary data for
urban functional zone (UFZ) extraction. However, current methods only use single data or …

Abstract The city of Yerevan, Armenia has undergone major environmental and economic
changes after the collapse of the Soviet Union. The objectives of this study were to:(i) …

Abstract Land Use and Land Cover (LULC) mapping products are essential for various
environmental studies, including ecological environmental assessments, resource …

Currently, ensemble approaches based, among other things, on the use of non-network
models are powerful tools for solving data analysis problems in various practical …

Accurate land use and land cover (LULC) is crucial for sustainable urban planning and for
many scientific researches. However, the demand for accurate LULC maps is increasing; it …