The use of machine learning techniques in classification problems has been shown to be useful in many applications. In particular, they have become increasingly popular in land cover mapping applications in the last decade. These maps often play an important role in environmental science applications as they can act as inputs within wider modelling chains and in estimating how the overall prevalence of particular land cover types may be changing
AbstractLand cover and land use classifications from remote sensing are increasingly becoming instit...
The aim of this article is to assess if the data provided by soft classifiers and uncertainty measur...
The classification of remotely sensed images such as aerial photographs or satellite sensor images f...
The use of machine learning techniques in classification problems has been shown to be useful in man...
The use of machine learning techniques in classification problems has been shown to be useful in man...
The use of land cover mappings built using remotely sensed imagery data has become increasingly popu...
Mappings play an important role in environmental science applications by allowing practitioners to m...
The use of land cover mappings built using remotely sensed imagery data has become increasingly popu...
Fonte, C. C., & Gonçalves, L. M. S. (2018). Identification of low accuracy regions in land cover map...
Uncertainty is one of the most essential and fundamental issues that requires full attention in almo...
Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment...
Land cover data derived from satellites are commonly used to prescribe inputs to models of the land ...
Abstract The purpose of this paper is to quantify uncertainty associated with land cover maps derive...
Training machine learning algorithms for land cover classification is labour intensive. Applying act...
In monitoring land cover change by overlay of two maps from different dates, the rate of change is f...
AbstractLand cover and land use classifications from remote sensing are increasingly becoming instit...
The aim of this article is to assess if the data provided by soft classifiers and uncertainty measur...
The classification of remotely sensed images such as aerial photographs or satellite sensor images f...
The use of machine learning techniques in classification problems has been shown to be useful in man...
The use of machine learning techniques in classification problems has been shown to be useful in man...
The use of land cover mappings built using remotely sensed imagery data has become increasingly popu...
Mappings play an important role in environmental science applications by allowing practitioners to m...
The use of land cover mappings built using remotely sensed imagery data has become increasingly popu...
Fonte, C. C., & Gonçalves, L. M. S. (2018). Identification of low accuracy regions in land cover map...
Uncertainty is one of the most essential and fundamental issues that requires full attention in almo...
Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment...
Land cover data derived from satellites are commonly used to prescribe inputs to models of the land ...
Abstract The purpose of this paper is to quantify uncertainty associated with land cover maps derive...
Training machine learning algorithms for land cover classification is labour intensive. Applying act...
In monitoring land cover change by overlay of two maps from different dates, the rate of change is f...
AbstractLand cover and land use classifications from remote sensing are increasingly becoming instit...
The aim of this article is to assess if the data provided by soft classifiers and uncertainty measur...
The classification of remotely sensed images such as aerial photographs or satellite sensor images f...