Recent advances of hyperspectral imaging technology and applications in agriculture
Remote sensing is a useful tool for monitoring spatio-temporal variations of crop
morphological and physiological status and supporting practices in precision farming. In …
morphological and physiological status and supporting practices in precision farming. In …
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
Land-use land-cover classification by machine learning classifiers for satellite observations—A review
Rapid and uncontrolled population growth along with economic and industrial development,
especially in develo** countries during the late twentieth and early twenty-first centuries …
especially in develo** countries during the late twentieth and early twenty-first centuries …
Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made …
Sudden-onset natural and man-made disasters represent a threat to the safety of human life
and property. Rapid and accurate building damage assessment using bitemporal high …
and property. Rapid and accurate building damage assessment using bitemporal high …
Multiattention network for semantic segmentation of fine-resolution remote sensing images
Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …
applications, including land resource management, biosphere monitoring, and urban …
[HTML][HTML] Deep learning for urban land use category classification: A review and experimental assessment
Map** the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …
valuable role in understanding urban environmental dynamics and facilitating sustainable …
ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection
Multi-temporal high spatial resolution earth observation makes it possible to detect complex
urban land surface changes, which is a significant and challenging task in remote sensing …
urban land surface changes, which is a significant and challenging task in remote sensing …
Multistage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images
The attention mechanism can refine the extracted feature maps and boost the classification
performance of the deep network, which has become an essential technique in computer …
performance of the deep network, which has become an essential technique in computer …
Assessment of land use land cover changes and future predictions using CA-ANN simulation for selangor, Malaysia
Land use land cover (LULC) has altered dramatically because of anthropogenic activities,
particularly in places where climate change and population growth are severe. The …
particularly in places where climate change and population growth are severe. The …