Machine learning offers the potential for effective and efficient classification of remotely
sensed imagery. The strengths of machine learning include the capacity to handle data of …

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth.
The current (and soon to be flown) generation of spaceborne and airborne optical sensors …

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 …

Timely and accurate delineation of global urban land is fundamental to the understanding of
global environmental changes. However, most of the contemporary global urban land maps …

Deriving land cover from remotely sensed data is fundamental to many operational mapping
and reporting programs as well as providing core information to support science activities …

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 …

Land cover and its change are crucial for many environmental applications. This study
focuses on the land cover classification and change detection with multitemporal and …

The accuracy of a classification is fundamental to its interpretation, use and ultimately
decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the …

Machine learning classifiers are being increasingly used nowadays for Land Use and Land
Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of …

Accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned
with the ongoing efforts to achieve sustainable development goals (SDGs) under …