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Carbon. --- Data acquisition. --- Forests. --- Image classification. --- Remote sensing.

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Deep learning has been revolutionizing many fields in computer vision, and facial informatics is one of the major fields. Novel approaches and performance breakthroughs are often reported on existing benchmarks. As the performances on existing benchmarks are close to saturation, larger and more challenging databases are being made and considered as new benchmarks, further pushing the advancement of the technologies. Considering face recognition, for example, the VGG-Face2 and Dual-Agent GAN report nearly perfect and better-than-human performances on the IARPA Janus Benchmark A (IJB-A) benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A (IJB-C), QMUL-SurvFace and MegaFace, are accepted as new standards for evaluating the performance of a new approach. Such an evolution is also seen in other branches of face informatics. In this Special Issue, we have selected the papers that report the latest progresses made in the following topics: 1. Face liveness detection 2. Emotion classification 3. Facial age estimation 4. Facial landmark detection We are hoping that this Special Issue will be beneficial to all fields of facial informatics.

History of engineering & technology --- deep learning --- RGB --- depth --- facial landmarking --- merging networks --- 3D geometry data --- 2D attribute maps --- fused CNN feature --- coarse-to-fine --- convolutional neural network (CNN) --- deep metric learning --- multi-task learning --- image classification --- age estimation --- generative adversarial network --- emotion classification --- facial key point detection --- facial images processing --- convolutional neural networks --- face liveness detection --- convolutional neural network --- thermal image --- external knowledge

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This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Information technology industries --- machine learning --- deep learning --- image processing --- classification --- tea --- fermentation --- automated image coding --- data collection methods --- interdisciplinary learning theory --- research methods --- systematic literature review --- visitor use management --- image classification --- multi-instance learning --- divergence --- dissimilarity --- bag-to-class --- Kullback–Leibler --- segment-based temporal modeling --- two-stream network --- action recognition --- internet of things --- detection --- dataset --- plant disease recognition --- image segmentation --- aphid --- Aphoidea --- lemon --- breast cancer mammogram dataset --- ultrasound breast cancer scans --- BI-RADS --- clinical data

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As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- mobile mapping system --- RRI model --- high-water marks --- inundation --- Northern Kyushu floods --- point clouds --- flood mapping --- temporary flooded vegetation (TFV) --- Sentinel-1 --- time series data --- Synthetic Aperture Radar (SAR) --- sentinel-1 --- SAR --- flood --- image classification --- clustering --- monsoon --- Philippines --- LiDAR --- geometric parameters --- levee stability --- overtopping --- Pearl River Delta --- CYGNSS --- flood detection --- Sistan and Baluchestan --- GNSS-R --- flood monitoring --- ALOS 2 --- multi-sensor integration --- multi-temporal inundation analysis --- Zambesi-Shire river basin --- image processing --- hydrology --- synthetic aperture radar --- n/a

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The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.

Research & information: general --- synthetic aperture radar --- despeckling --- multi-scale --- LSTM --- sub-pixel --- high-resolution remote sensing imagery --- road extraction --- machine learning --- DenseUNet --- scene classification --- lifting scheme --- convolution --- CNN --- image classification --- deep features --- hand-crafted features --- Sinkhorn loss --- remote sensing --- text image matching --- triplet networks --- EfficientNets --- LSTM network --- convolutional neural network --- water identification --- water index --- semantic segmentation --- high-resolution remote sensing image --- pixel-wise classification --- result correction --- conditional random field (CRF) --- satellite --- object detection --- neural networks --- single-shot --- deep learning --- global convolution network --- feature fusion --- depthwise atrous convolution --- high-resolution representations --- ISPRS vaihingen --- Landsat-8 --- faster region-based convolutional neural network (FRCNN) --- single-shot multibox detector (SSD) --- super-resolution --- remote sensing imagery --- edge enhancement --- satellites --- open-set domain adaptation --- adversarial learning --- min-max entropy --- pareto ranking --- SAR --- Sentinel–1 --- Open Street Map --- U–Net --- desert --- road --- infrastructure --- mapping --- monitoring --- deep convolutional networks --- outline extraction --- misalignments --- nearest feature selector --- hyperspectral image classification --- two stream residual network --- Batch Normalization --- plant disease detection --- precision agriculture --- UAV multispectral images --- orthophotos registration --- 3D information --- orthophotos segmentation --- wildfire detection --- convolutional neural networks --- densenet --- generative adversarial networks --- CycleGAN --- data augmentation --- pavement markings --- visibility --- framework --- urban forests --- OUDN algorithm --- object-based --- high spatial resolution remote sensing --- Generative Adversarial Networks --- post-disaster --- building damage assessment --- anomaly detection --- Unmanned Aerial Vehicles (UAV) --- xBD --- feature engineering --- orthophoto --- unsupervised segmentation