Land-use mapping and crop classification have both benefited greatly from the analysis of
high-resolution remote sensing photos based on deep learning. This research proposes …

Crop Classification using remote sensing data is important for calculating crop sown area
and predicting the crop production. Accuracy in data will help to regulate marketing of the …

The world's growing population leads the government of Pakistan to increase the supply of
food for the coming years in a well-organized manner. Feasible agriculture plays a vital role …

Classification of multispectral images is a processing tool used in various applications of
remote sensing in natural hazards assessment, agricultural and environmental monitoring to …

Multispectral remote sensing images (MRSI) are widely employed to assess modifications in
water bodies, land use and land cover changes, forest degradation, landscape change, and …

Generation of a thematic map is important for scientists and agriculture engineers in
analyzing different crops in a given field. Remote sensing data are well-accepted for image …

Remote sensing image analysis is an emerging area of research and is used for various
applications such as climate analysis, crop monitoring and change detection. Hyperspectral …

Deep learning convolutional neural network (CNN) is popular as being widely used for
classification of unstructured data. Land use land cover (LULC) classification using remote …

Weed classification and detection is an important and critical step in the area of weed
control. As weeds exist in all fields and in all seasons, it is required to perform weed control …

Land use and land cover plays a vital role in the natural resource management. It is used to
map the environmental changes for ecosystem monitoring. Automatic mapping can bring …