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Selected papers from the International Conference on Transportation and Development 2019, held in Alexandria, Virginia, June 9–12, 2019. Sponsored by the Transportation & Development Institute of ASCE.The collection contains 17 peer-reviewed papers on innovation and sustainability in smart mobility and smart cities.Topics include: tracking and sensing vehicles in smart mobility; accessibility in smart cities; new energy in smart mobility; air transportation and unmanned aerial vehicles; and international topics in smart mobility.This proceedings will be of interest to practitioners, planners, researchers, and policymakers in a broad area of smart transportation and development.

Smart cities --- Intelligent transportation systems --- Transportation --- Communication and traffic --- Innovation --- Sustainable development --- Data collection --- Unmanned aerial vehicles (UAV) --- Tracking --- Sensors and sensing --- Air transportation --- Aircraft and spacecraft --- Technological innovations --- Management

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Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry.

unmanned aerial vehicles --- seedling detection --- forest regeneration --- reforestation --- establishment survey --- machine learning --- multispectral classification --- UAV photogrammetry --- forest modeling --- ancient trees measurement --- tree age prediction --- Mauritia flexuosa --- semantic segmentation --- end-to-end learning --- convolutional neural network --- forest inventory --- Unmanned Aerial Systems (UAS) --- structure from motion (SfM) --- Unmanned Aerial Vehicles (UAV) --- Photogrammetry --- Thematic Mapping --- Accuracy Assessment --- Reference Data --- Forest Sampling --- Remote Sensing --- Robinia pseudoacacia L. --- reproduction --- spreading --- short rotation coppice --- unmanned aerial system (UAS) --- object-based image analysis (OBIA) --- convolutional neural network (CNN) --- juniper woodlands --- ecohydrology --- remote sensing --- unmanned aerial systems --- central Oregon --- rangelands --- seedling stand inventorying --- photogrammetric point clouds --- hyperspectral imagery --- leaf-off --- leaf-on --- UAV --- multispectral image --- forest fire --- burn severity --- classification --- precision agriculture --- biomass evaluation --- image processing --- Castanea sativa --- unmanned aerial vehicles (UAV) --- precision forestry --- forestry applications --- RGB imagery

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This book is the first literature collection focused on the development and implementation of unmanned aircraft systems (UAS) and their integration with sensors for atmospheric measurements on Earth. The research covered in the book combines chemical, physical, and meteorological measurements performed in field campaigns, as well as conceptual and laboratory work. Useful examples for the development of platforms and autonomous systems for environmental studies are provided, which demonstrate how careful the operation of sensors aboard UAS must be to gather information for remote sensing in the atmosphere. The work serves as a key collection of articles to introduce the topic to new researchers interested in the field, guide future studies, and motivate measurements to improve our understanding of the Earth’s complex atmosphere.

Research & information: general --- unmanned aerial vehicles (UAV) --- drones --- geostatistics --- atmospheric physics --- meteorology --- spatial sampling --- unmanned aerial vehicles --- unmanned aerial systems, turbulence --- atmospheric boundary layer --- TK-1G sounding rocket --- near space --- data analysis --- remote sensing --- unmanned aerial systems --- atmospheric composition --- sensors --- UAS --- RPAS --- ALADINA --- airborne turbulence --- radiation measurements --- aerosol measurements --- field experiments --- validation methods --- unmanned aircraft --- meteorological observation --- stable atmospheric boundary layer --- turbulence --- remotely piloted aircraft systems (RPAS) --- ground-based in-situ observations --- boundary layer remote sensing --- Arctic --- polar --- sea ice --- n/a --- source estimation --- methane emissions --- natural gas --- leak surveys --- inverse emissions --- MONITOR --- UAV --- LDAR --- air pollution --- unmanned aerial vehicle (UAV) --- PM2.5 --- meteorological condition --- long-distance transport --- satellite data --- RMLD-UAV --- methane --- mass flux --- leak rate quantification --- wind speed and direction estimation algorithms --- flow probes --- airspeed measurement --- small unmanned aircraft systems (sUAS)

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This book is the first literature collection focused on the development and implementation of unmanned aircraft systems (UAS) and their integration with sensors for atmospheric measurements on Earth. The research covered in the book combines chemical, physical, and meteorological measurements performed in field campaigns, as well as conceptual and laboratory work. Useful examples for the development of platforms and autonomous systems for environmental studies are provided, which demonstrate how careful the operation of sensors aboard UAS must be to gather information for remote sensing in the atmosphere. The work serves as a key collection of articles to introduce the topic to new researchers interested in the field, guide future studies, and motivate measurements to improve our understanding of the Earth’s complex atmosphere.

Research & information: general --- unmanned aerial vehicles (UAV) --- drones --- geostatistics --- atmospheric physics --- meteorology --- spatial sampling --- unmanned aerial vehicles --- unmanned aerial systems, turbulence --- atmospheric boundary layer --- TK-1G sounding rocket --- near space --- data analysis --- remote sensing --- unmanned aerial systems --- atmospheric composition --- sensors --- UAS --- RPAS --- ALADINA --- airborne turbulence --- radiation measurements --- aerosol measurements --- field experiments --- validation methods --- unmanned aircraft --- meteorological observation --- stable atmospheric boundary layer --- turbulence --- remotely piloted aircraft systems (RPAS) --- ground-based in-situ observations --- boundary layer remote sensing --- Arctic --- polar --- sea ice --- source estimation --- methane emissions --- natural gas --- leak surveys --- inverse emissions --- MONITOR --- UAV --- LDAR --- air pollution --- unmanned aerial vehicle (UAV) --- PM2.5 --- meteorological condition --- long-distance transport --- satellite data --- RMLD-UAV --- methane --- mass flux --- leak rate quantification --- wind speed and direction estimation algorithms --- flow probes --- airspeed measurement --- small unmanned aircraft systems (sUAS)

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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