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This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society.

Research & information: general --- earthquake --- damaged groups of buildings --- classification --- remote sensing images --- Convolution Neural Network (CNN) --- block vector data --- shoreline change --- landsat --- planet scope --- coastline --- morphological changes --- building extraction --- improved anchor-free instance segmentation --- high-resolution remote sensing images --- deep learning --- land use/land cover (LULC) --- GF-6 WFV --- object-oriented --- change detection --- double constraints --- REE mines --- mining and restoration assessment indicators (MRAIs) --- damage --- time trajectory --- effectiveness of management --- aeolian process --- desertification --- multi-sensor fusion --- interferometric SAR --- time-series analysis --- mussel farming --- high-resolution image --- transitional water management --- environmental pollution --- open source software --- synthetic aperture radar (SAR) --- target --- sea surface --- multiple scattering --- geo-hazard mapping --- Gaofen-1 satellite --- land cover --- environmental factors --- susceptibility --- post-classification differencing --- generalized difference vegetation index (GDVI) --- multiple linear regression --- logistic regression --- n/a

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Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.

Research & information: general --- depthwise separable convolution (DSC) --- all convolutional network (ACN) --- batch normalization (BN) --- ensemble convolutional neural network (ECNN) --- electrocardiogram (ECG) --- MIT-BIH database --- cephalometric landmark --- X-ray --- deep learning --- ResNet --- registration --- electronic human-machine interface --- blindness --- gesture recognition --- inertial sensors --- IMU --- dynamic contrast-enhanced MRI --- kidney perfusion --- glomerular filtration rate --- pharmacokinetic modeling --- multi-layer perceptron --- parameter estimation --- instance segmentation --- computer vision --- retinal blood vessel image --- computer-aided diagnosis --- U-shaped neural network --- residual learning --- semantic gap --- intracranial hemorrhage --- computed tomography --- random forest --- sleep disorder --- obstructive sleep disorder --- overnight polysomnogram --- EEG --- EMG --- ECG --- HRV signals --- Electronic Medical Record (EMR) --- disease prediction --- Amyotrophic Lateral Sclerosis (ALS) --- weighted Jaccard index (WJI) --- lung cancer --- CT images --- CNN --- pulmonary fibrosis --- radiotherapy --- n/a

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Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management.

Technology: general issues --- History of engineering & technology --- pig weight --- body size --- estimation --- deep learning --- convolutional neural network --- pig identification --- mask scoring R-CNN --- soft-NMS --- group-housed pigs --- audio --- dairy cow --- mastication --- jaw movement --- forage management --- precision livestock management --- equine behavior --- wearable sensor --- intermodality interaction --- class-balanced focal loss --- absorbing Markov chain --- cow behavior analysis --- prediction of calving time --- cow identification --- EfficientDet --- YOLACT++ --- cascaded model --- instance segmentation --- generative adversarial network --- machine learning --- automated medical image processing --- deep neural network --- animal science --- CT scans --- computer vision --- cow --- extensive livestock --- sensorized wearable device --- monitoring --- parturition prediction --- radar sensors --- radar signal processing --- animal farming --- computational ethology --- signal classification --- wavelet analysis --- dairy welfare --- hierarchical clustering --- mutual information --- precision livestock farming --- time budgets --- unsupervised machine learning --- wearables design --- animal-centered design --- animal telemetry --- modularity --- smart collar --- design contributions --- additive manufacturing --- low-frequency tracking --- commercial aviary --- laying hens --- false registrations --- tree-based classifier --- animal behaviour

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Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management.

pig weight --- body size --- estimation --- deep learning --- convolutional neural network --- pig identification --- mask scoring R-CNN --- soft-NMS --- group-housed pigs --- audio --- dairy cow --- mastication --- jaw movement --- forage management --- precision livestock management --- equine behavior --- wearable sensor --- intermodality interaction --- class-balanced focal loss --- absorbing Markov chain --- cow behavior analysis --- prediction of calving time --- cow identification --- EfficientDet --- YOLACT++ --- cascaded model --- instance segmentation --- generative adversarial network --- machine learning --- automated medical image processing --- deep neural network --- animal science --- CT scans --- computer vision --- cow --- extensive livestock --- sensorized wearable device --- monitoring --- parturition prediction --- radar sensors --- radar signal processing --- animal farming --- computational ethology --- signal classification --- wavelet analysis --- dairy welfare --- hierarchical clustering --- mutual information --- precision livestock farming --- time budgets --- unsupervised machine learning --- wearables design --- animal-centered design --- animal telemetry --- modularity --- smart collar --- design contributions --- additive manufacturing --- low-frequency tracking --- commercial aviary --- laying hens --- false registrations --- tree-based classifier --- animal behaviour

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The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- pine wilt disease dataset --- GIS application visualization --- test-time augmentation --- object detection --- hard negative mining --- video synthetic aperture radar (SAR) --- moving target --- shadow detection --- deep learning --- false alarms --- missed detections --- synthetic aperture radar (SAR) --- on-board --- ship detection --- YOLOv5 --- lightweight detector --- remote sensing image --- spectral domain translation --- generative adversarial network --- paired translation --- synthetic aperture radar --- ship instance segmentation --- global context modeling --- boundary-aware box prediction --- land-use and land-cover --- built-up expansion --- probability modelling --- landscape fragmentation --- machine learning --- support vector machine --- frequency ratio --- fuzzy logic --- artificial intelligence --- remote sensing --- interferometric phase filtering --- sparse regularization (SR) --- deep learning (DL) --- neural convolutional network (CNN) --- semantic segmentation --- open data --- building extraction --- unet --- deeplab --- classifying-inversion method --- AIS --- atmospheric duct --- ship detection and classification --- rotated bounding box --- attention --- feature alignment --- weather nowcasting --- ResNeXt --- radar data --- spectral-spatial interaction network --- spectral-spatial attention --- pansharpening --- UAV visual navigation --- Siamese network --- multi-order feature --- MIoU --- imbalanced data classification --- data over-sampling --- graph convolutional network --- semi-supervised learning --- troposcatter --- tropospheric turbulence --- intercity co-channel interference --- concrete bridge --- visual inspection --- defect --- deep convolutional neural network --- transfer learning --- interpretation techniques --- weakly supervised semantic segmentation --- n/a