Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This collection amplifies the experiences of some of the world’s young people who are working to address SDGs using geospatial technologies and multi-national collaboration. Authors from every region of the world who have emerged as leaders in the YouthMappers movement share their perspectives and knowledge in an accessible and peer-friendly format. YouthMappers are university students who create and use open mapping for development and humanitarian purposes. Their work leverages digital innovations - both geospatial platforms and communications technologies - to answer the call for leadership to address sustainability challenges. The book conveys a sense of robust knowledge emerging from formal studies or informal academic experiences - in the first-person voices of students and recent graduates who are at the forefront of creating a new map of the world. YouthMappers use OpenStreetMap as the foundational sharing mechanism for creating data together. Authors impart the way they are learning about themselves, about each other, about the world. They are developing technology skills, and simultaneously teaching the rest of the world about the potential contributions of a highly connected generation of emerging world leaders for the SDGs. The book is timely, in that it captures a pivotal moment in the trajectory of the YouthMappers movement’s ability to share emerging expertise, and one that coincides with a pivotal moment in the geopolitical history of planet earth whose inhabitants need to hear from them. Most volumes that cover the topic of sustainability in terms of youth development are written by non-youth authors. Moreover, most are written by non-majoritarian, entrenched academic scholars. This book instead puts forward the diverse voices of students and recent graduates in countries where YouthMappers works, all over the world. Authors cover topics that range from water, agriculture, food, to waste, education, gender, climate action and disasters from their own eyes in working with data, mapping, and humanitarian action, often working across national boundaries and across continents. To inspire readers with their insights, the chapters are mapped to the United Nations 17 Sustainable Development Goals (SDGs) in ways that connect a youth agenda to a global agenda. This is an open access book. .

Geography. --- Digital humanities. --- Open source software. --- Sustainability. --- Cartography. --- Digital Humanities. --- Open Source. --- Cartography, Primitive --- Chartography --- Map-making --- Mapmaking --- Mapping (Cartography) --- Mathematical geography --- Surveying --- Map projection --- Maps --- Sustainability science --- Human ecology --- Social ecology --- Free software (Open source software) --- Open code software --- Opensource software --- Computer software --- Humanities --- Cosmography --- Earth sciences --- World history --- YouthMappers --- Sustainable development goals --- Open street map --- Open spatial data --- Community engagement --- Youth empowerment

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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