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Natural hazard events are able to significantly affect the natural and artificial environment. In this context, changes in landforms due to natural disasters have the potential to affect and, in some cases, even restrict human interaction with the ecosystem. In order to minimize fatalities and reduce the economic impact that accompanies their occurrence, proper planning is crucial. Land use planning can play an important role in reducing current and future risks related to natural hazards. Land use changes can lead to natural hazards and vice versa: natural hazards affect land uses. Therefore, planners may take into account areas that are susceptible to natural hazards when selecting favorable locations for land use development. Appropriate land use planning can lead to the determination of safe and non-safe areas for urban activities. This Special Issue focuses on land use planning for natural hazards. In this context, various types of natural hazards, such as land degradation and desertification, coastal hazard, floods, and landslides, as well as their interactions with human activities, are presented.

Research & information: general --- sea-level rise --- storm surge --- physical vulnerability --- social vulnerability --- Peloponnese --- Greece --- urbanization --- flood --- remote sensing/GIS --- Birendranagar --- Nepal --- landslides --- geographic information system (GIS) --- frequency ratio --- density ratio --- human activities --- land use planning --- historic flood data --- old topographic maps --- GIS --- temporal and spatial distribution of flood events --- marshy areas and lakes --- flood hazard assessment --- Integrated land-use planning --- land degradation --- desertification --- policy --- phronetic approach --- n/a

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This book focuses on the use of GIScience in conjunction with historical visual sources to resolve past scenarios. The themes, knowledge gained and methodologies conducted might be of interest to a variety of scholars from the social science and humanities disciplines.

land use/land cover (LULC) --- landscapes --- historical maps --- Geographic Information System (GIS) --- agriculture --- vineyards --- olive groves --- Ein Karem --- Bethlehem --- Hebron --- urban geomorphology --- anthropogenic landforms --- old maps --- contour lines --- Genoa --- historical GIS --- HGIS --- GIS tools --- fishnet --- grid --- urban morphology --- Inoh’s map --- coastlines --- terrain --- land use --- uncertainty --- visibility --- topographic accessibility --- Central Europe --- information system --- Vltava River --- geolocation --- photographs --- historical visual sources --- graph embeddings --- geospatial descriptors --- indexing and retrieval of historical data --- GIS --- carbon balance --- rural landscape --- total environment --- historical geography --- GIScience --- visual sources --- spatial approaches --- cartography

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The transition towards renewable energy sources and “green” technologies for energy generation and storage is expected to mitigate the climate emergency in the coming years. However, in many cases, this progress has been hampered by our dependency on critical materials or other resources that are often processed at high environmental burdens. Yet, many studies have shown that environmental and energy issues are strictly interconnected and require a comprehensive understanding of resource management strategies and their implications. Life cycle assessment (LCA) is among the most inclusive analytical techniques to analyze sustainability benefits and trade-offs within complex systems and, in this Special Issue, it is applied to assess the mutual influences of environmental and energy dimensions. The selection of original articles, reviews, and case studies addressed covers some of the main driving applications for energy requirements and greenhouse gas emissions, including power generation, bioenergy, biorefinery, building, and transportation. An insightful perspective on the current topics and technologies, and emerging research needs, is provided. Alone or in combination with integrative methodologies, LCA can be of pivotal importance and constitute the scientific foundation on which a full system understanding can be reached.

Research & information: general --- life cycle assessment --- harmonization --- photovoltaic --- perovskite solar cell --- manufacturing process --- environmental impact --- greenhouse gas --- gasification --- swine manure management --- ground-source heat pumps --- space conditioning --- environmental sustainability --- life cycle assessment (LCA) --- phase-change material (PCM) --- CED --- Eco-indicator 99 --- IPCC --- LCA --- photovoltaics panels --- recycling --- landfill --- embodied energy --- embodied carbon --- life-cycle embodied performance --- metropolitan area --- in-city --- transport energy intensity --- well to wheel --- material structure --- photovoltaics --- waste management --- EROI --- net energy --- energy scenario --- energy transition --- electricity --- grid mix --- storage --- decarbonization --- biofuel policy --- GHG mitigation --- energy security --- indirect land use change --- carbon dioxide capture --- activated carbon --- environmental impacts --- Life Cycle Assessment (LCA) --- Material Flow Analysis (MFA) --- Criticality --- traction batteries --- forecast --- supply --- exergy --- sustainability --- review --- bioenergy --- geographic information system (GIS) --- harvesting residues --- energy metrics --- PHAs --- bio-based polymers --- biodegradable plastics --- pyrolysis --- volatile fatty acids --- phase change materials --- PCM --- thermal energy storage --- Storage LCA Tool --- Speicher LCA --- n/a

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

Research & information: general --- convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN --- convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN