The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and
decision making in the food industry and in agro-environmental management. The global …

Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital
role in many environment modeling and land-use inventories. In this study, a hybrid feature …

Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

The extraction and classification of crops is the core issue of agricultural remote sensing.
The precise classification of crop types is of great significance to the monitoring and …

In comparison with conventional machine learning algorithms, deep learning can effectively
express the deep features of remote sensing images. Considering the rich spectral and …

The identification and counting of grassland degradation indicator ground objects is an
important component of grassland ecological monitoring. These steps are also an important …

Hyperspectral imaging instruments could capture detailed spatial information and rich
spectral signs of observed scenes. Much spatial information and spectral signatures of …

In recent years, the use of remote sensing data obtained from satellite or unmanned aerial
vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield …

Green space in cities has been reducing rapidly due to the intensive urban expansion,
which contributes to surface temperature growth, leading to numerous challenges in …