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Coastal environments are dynamic ecosystems, where erosion is influenced by meteorological/climatic, geological, biological, and anthropic factors. Erosion has worrying effects on the environment, infrastructure, lifelines, and buildings. Furthermore, climate change is exacerbating an already fragile situation. We are witnessing a high-risk situation and are convinced that this is the most appropriate time to focus on state-of-the-art remote sensing techniques for shoreline monitoring. The improvements in the spatial and spectral resolution of current and next generation satellite-based sensors and the significant progress in the spatial data processing identify remote sensing techniques that increase our knowledge of territory and coastline. This Special Issue aims to highlight an overview of all multiscale remote sensing techniques (e.g., high resolution images, photogrammetry, SAR, etc.) and a whole array of methods and techniques that process, analyse, and discuss multitemporal remotely sensed data. Thank you to all of our contributors and authors for their interesting and illuminating studies. Since this topic is complex and dynamic, we hope to develop this research with future works to form more cutting-edge studies.

History of engineering & technology --- DGPS measurements --- video camera observation --- shoreline position --- beach survey --- Sentinel-2 --- Remote Sensing --- habitat mapping --- mangroves --- coral reefs --- climate change --- vulnerable habitats --- side-scan sonar --- swath bathymetry --- habitat monitoring --- hurricane Sandy --- hurricane Joaquin --- shoreline detection --- remote sensing --- WorldView-2 --- Abruzzo --- multispectral classification --- shoreline --- coastline --- satellite images --- synthetic aperture radar (SAR) --- Sentinel-1 --- shoreline extraction --- coastline extraction --- active connection matrix (ACM) --- J-Net Dynamic --- edge detection --- canny edge detector --- coastline mapping --- geomatics --- SfM photogrammetry --- network RTK --- sea level rise --- coastlines --- 2100 --- storm surges --- heritage sites --- Pyrgi --- Mediterranean --- UAV --- DSM --- n/a

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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 recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms.

multi-camera system --- space alignment --- UAV-assisted calibration --- cross-view matching --- spatiotemporal feature map --- view-invariant description --- air-to-ground synchronization --- tidal flat water --- YOLOv3 --- similarity algorithm for water extraction --- arbitrary-oriented object detection in satellite optical imagery --- adaptive dynamic refined single-stage transformer detector --- feature pyramid transformer --- dynamic feature refinement --- synthetic aperture radar (SAR) --- ship detection --- convolutional neural network (CNN) --- deep learning (DL) --- feature pyramid network (FPN) --- quad feature pyramid network (Quad-FPN) --- crowd estimation --- 3D simulation --- unmanned aerial vehicle --- synthetic crowd data --- invasive species --- thermal imaging --- habitat identification --- deep learning --- drone --- multiview semantic vegetation index --- urban forestry --- green view index (GVI) --- semantic segmentation --- urban vegetation --- RGB vegetation index --- n/a

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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

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The Special Issue entitled “Remote Sensing in Vessel Detection and Navigation” comprises 15 articles on many topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used. It can be said that navigation and vessel detection remain important and hot topics, and a lot of work will continue to be done worldwide. New techniques and methods for analyzing and extracting information from navigational sensors and data have been proposed and verified. Some of these will spark further research, and some are already mature and can be considered for industrial implementation and development.

Research & information: general --- autonomous navigation --- automatic radar plotting aid --- safe objects control --- game theory --- computer simulation --- Sentinel-2 --- multispectral --- temporal offsets --- ship --- aircraft --- velocity --- altitude --- parallax --- jet stream --- Unmanned Surface Vessel (USV) --- multi-Global Navigation Satellite System (GNSS) receiver --- bathymetric measurements --- cross track error (XTE) --- SSL --- six-degrees-of-freedom motion --- motion attitude model --- edge detection --- straight-line fitting --- visual saliency --- vessel detection --- video monitoring --- inland waterway --- real-time detection --- neural network --- target recognition --- HRRP --- residual structure --- loss function --- trajectory tracking --- unmanned surface vehicle --- navigation --- bathymetry --- hydrographic survey --- real-time communication --- maritime situational awareness --- ship detection --- Iridium --- on-board --- image processing --- flight campaign --- position estimation --- ranging mode --- single shore station --- AIS --- bag-of-words mechanism --- machine learning --- image analysis --- ship classification --- marine system --- river monitoring system --- feature extraction --- synthetic aperture radar (SAR) ship detection --- multi-stage rotational region based network (MSR2N) --- rotated anchor generation --- multi-stage rotational detection network (MSRDN) --- convolutional neural network (CNN) --- synthetic aperture radar (SAR) --- multiscale and small ship detection --- complex background --- false alarm --- farbon dioxide peaks --- midwave infrared --- FTIR --- adaptive stochastic resonance (ASR) --- matched intrawell response --- nonlinear filter --- line enhancer --- autonomous underwater vehicles (AUVs) --- target tracking --- group targets --- GLMB --- structure --- formation --- remote sensing