This book constitutes the refereed proceedings of the 6th International Conference on Information Management and Big Data, SIMBig 2019, held in Lima, Peru, in August 2019. The 15 full papers and 16 short papers presented were carefully reviewed and selected from 104 submissions. The papers address issues such as data mining, artificial intelligence, Natural Language Processing, information retrieval, machine learning, web mining.
This handbook provides an exhaustive, one-stop reference and a state-of-the-art description of geographic information and its use. This new, substantially updated edition presents a complete and rigorous overview of the fundamentals, methods and applications of the multidisciplinary field of geographic information systems. Designed to be a useful and readable desk reference book, but also prepared in various electronic formats, this title allows fast yet comprehensive review and easy retrieval of essential reliable key information. The Springer Handbook of Geographic Information is divided into three parts. Part A, Basics and Computer Science, provides an overview on the fundamentals, including descriptions of databases and encoding of geographic information. It also covers the underlying mathematical and statistics methods and modeling. A new chapter exemplifies the emerging use and analysis of big data in a geographic context. Part B offers rigorous descriptions of gathering, processing and coding of geographic information in a standardized way to allow interoperable use in a variety of systems; from traditional methods such as geodesy and surveying to state-of-the-art remote sensing and photogrammetry; from cartography to geospatial web services. Discussions on geosemantic interoperability and security of open distributed geospatial information systems complete the comprehensive coverage. The final part describes a wide array of applications in science, industry and society at large, such as agriculture, defense, transportation, energy and utilities, health and human services. The part is enhanced by new chapters on smart cities and building information modeling, as well as a complete overview of the currently available open-source geographic information systems. Using standardized international terminology, in accordance with ISO/TC 211 and INSPIRE, this handbook facilitates collaboration between different disciplines and is a must have for practitioners and new comers in industry and academia.
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences.
Today, the relentless depletion of natural resources has reached a critical juncture, demanding innovative solutions. Advanced Geospatial Practices in Natural Environment Resource Management dives into the intricate tapestry of issues jeopardizing ecosystems. This book systematically dissects the fundamental drivers, traces the historical evolution, and elucidates the underlying causes that have led to this precarious point. From deforestation to pollution, from climate change to habitat destruction, these challenges are multifaceted. By harnessing the power of data-driven decision-making and predictive modeling, this book advocates for a profound shift in the approach to environmental issues. This book also explores the complexities of water, soil, and air-related challenges, offering technically sound solutions that strike a balance between legislative, economic, social, and political considerations. The holistic approach championed in these pages holds the potential to benefit governments, businesses, and communities alike. This book is ideal for a diverse audience, including students, scholars, and researchers, as well as environmental science enthusiasts, geospatial technology professionals, and AI/ML practitioners. Policymakers, environmental engineers, and professionals engaged in governmental and corporate sustainability initiatives will also discover practical solutions to address the pressing challenges.
This book uses five decades of map data, air photos, and medium to high-resolution satellite imagery to track the expansions of aquaculture and the loss of both estuarine and mangrove land covers in Ecuador. The results are staggering. In some regions, Ecuador has lost almost 50% of its estuarine space and approximately 80% of its mangrove forest. The current estuarine land cover bears no resemblance to the historic estuarine land cover. The analysis is complete from 1968 to 2014. The analysis covers all the major estuaries of mainland Ecuador. The research expands beyond purely land cover into the land use of the estuaries and the implications of the land cover transitions. The author lived in Ecuador's estuarine environments for almost two years studying this area. During this time he conducted mapping workshops with local residents, conducted 100 interviews with local actors, conducted six group discussions with fisherfolk syndicates, conducted eight presentations, worked on a shrimp farm. He was employed by the Ministry of the Environment on a Prometeo fellowship for one-year researching estuarine health and worked on mangrove replanting projects in the estuaries. In addition to the remote sensing data, the author provides a contextual framework to the analysis. It is not just hard numbers that are presented, but a remote sensing analysis tied to local actors that tell a coherent almost 50 -year estuarine story at the national, provincial, and local scales The book is intended for researchers, academics, graduate students, NGOs, and government actors including those who work in development, environment, and policy implementation. It is suitable supplemental reading for students in courses related to the coastal zone, land use change, and remote sensing. The electronically supplementary material includes all the related data to underpin the analysis as well as all the resulting GIS files.
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.
