Image classification is a complex process that may be affected by many factors. This paper
examines current practices, problems, and prospects of image classification. The emphasis …

In the last three decades, the technologies and methods of remote sensing have evolved
dramatically to include a suite of sensors operating at a wide range of imaging scales with …

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and
object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be …

Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation map**
in complex urban landscapes due to the ultra-high resolution imagery acquired at low …

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to
map land covers over large geographic areas using supervised machine learning …

The accuracy of thematic maps derived by image classification analyses is often compared
in remote sensing studies. This comparison is typically achieved by a basic subjective …

PlanetScope (PL) high-resolution composite base maps have recently become available
within Google Earth Engine (GEE) for the tropical regions thanks to the partnership between …

Conventional approaches to training a supervised image classification aim to fully describe
all of the classes spectrally. To achieve a complete description of each class in feature …

Information on the rate and pattern of urban expansion is required by urban planners to
devise proper urban planning and management policy directions. This study evaluated the …

Understanding land use/cover (LULC) changes and their impacts on the catchment are
imperative for proper land management. Hence, useful information concerning responses to …