Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of
applications in the geospatial and remote sensing (RS) sciences, and consequently has …

Accuracy assessment and land cover mapping have been inexorably linked throughout the
first 50 years of publication of Remote Sensing of Environment. The earliest developers of …

Urban land use information is essential for a variety of urban-related applications such as
urban planning and regional administration. The extraction of urban land use from very fine …

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps
are needed to document the occurrence and extent of landslides and to investigate their …

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 …

Abstract The US Geological Survey Land Change Monitoring, Assessment and Projection
(USGS LCMAP) has released a suite of annual land cover and land cover change products …

Along with rapid urbanization, the growth and persistence of slums is a global challenge.
While remote sensing imagery is increasingly used for producing slum maps, only a few …

Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis
paradigm that defines and examines image-objects: groups of neighboring pixels that …

Despite the need for quality land cover information, large-area, high spatial resolution land
cover mapping has proven to be a difficult task for a variety of reasons including large data …