Decision makers, such as government officials, need to better understand human activity in order to make informed decisions. With the ability to measure and explore geographic space through the use of geospatial intelligence data sources including imagery and mapping data, they are better able to measure factors affecting the human population. As a broad field of study, geospatial research has applications in a variety of fields including military science, environmental science, civil engineering, and space exploration. Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications explores multidisciplinary applications of geographic information systems to describe, assess, and visually depict physical features and to gather data, information, and knowledge regarding human activity. Highlighting a range of topics such as geovisualization, spatial analysis, and landscape mapping, this multi-volume book is ideally designed for data scientists, engineers, government agencies, researchers, and graduate-level students in GIS programs.

This book explores cutting-edge methods combining geospatial technologies and artificial intelligence related to several fields such as smart farming, urban planning, geology, transportation, and 3D city models. It introduces techniques which range from machine and deep learning to remote sensing for geospatial data analysis. The book consists of two main parts that include 13 chapters contributed by promising authors. The first part deals with the use of artificial intelligence techniques to improve spatial data analysis, whereas the second part focuses on the use of artificial intelligence with remote sensing in various fields. Throughout the chapters, the interest for the use of artificial intelligence is demonstrated for different geospatial technologies such as aerial imagery, drones, Lidar, satellite remote sensing, and more. The work in this book is dedicated to the scientific community interested in the coupling of geospatial technologies and artificial intelligence and exploring the synergetic effects of both fields. It offers practitioners and researchers from academia, the industry and government information, experiences and research results about all aspects of specialized and interdisciplinary fields on geospatial intelligence.

Geospatial Intelligence: Origins and Evolution tells the story of how the current age of geospatial knowledge evolved from its ancient origins to become ubiquitous in daily life across the globe, weaving a tapestry of stories about the people, events, ideas, and technologies that affected the trajectory of what has become known as GEOINT.

The National Geospatial-Intelligence Agency (NGA) provides geospatial intelligence (GEOINT) to support national security, both as a national intelligence and a combat support agency. In the post-9/11 world, the need for faster and more accurate geospatial intelligence is increasing. GEOINT uses imagery and geospatial data and information to provide knowledge for planning, decisions, and action. For example, data from satellites, pilotless aircraft and ground sensors are integrated with maps and other intelligence data to provide location information on a potential target. This report defines 12 hard problems in geospatial science that NGA must resolve in order to evolve their capabilities to meet future needs. Many of the hard research problems are related to integration of data collected from an ever-growing variety of sensors and non-spatial data sources, and analysis of spatial data collected during a sequence of time (spatio-temporal data). The report also suggests promising approaches in geospatial science and related disciplines for meeting these challenges. The results of this study are intended to help NGA prioritize geospatial science research directions.

Geospatial Intelligence: Origins and Evolution tells the story of how the current age of geospatial knowledge evolved from its ancient origins to become ubiquitous in daily life across the globe, weaving a tapestry of stories about the people, events, ideas, and technologies that affected the trajectory of what has become known as GEOINT.

A Short Introduction to Geospatial Intelligence explains the newest form of intelligence used by governments, commercial organizations, and individuals. Geospatial intelligence combines late 20th century historically derived ways of thinking and early 21st century technologies of GIS, GPS, digital imaging satellites and communications satellites to identify, measure, and analyze the current risk in the world. These ways of thinking have developed from military engineering, cartography, photointerpretation, and imagery analysis. While the oldest example dates back to the early 16th century, all the ways of spatial thinking share the common thread of being developed and refined during conflicts to help military leaders make informed decisions prior to action. In the 21st century— thanks in great part to advances in digital precision technology, miniaturization, and the commercialization of satellites— these ways of thinking have expanded from the military into various other industries and sectors including energy, agriculture, environment, law enforcement, global risk assessment, and climate monitoring. Features: • Analyzes human and algorithmic models for dealing with the challenge of analytic attention, in an age of geospatial data overload • Establishes an original model— envisioning, discovery, recording, comprehending, and tracking— for the spatial thinking that underpins the practice and growth of this emerging discipline • Addresses the effects of small satellites on the collection and analysis of geospatial intelligence A Short Introduction to Geospatial Intelligence describes the development of the five steps in geospatial thinking— envisioning, discovery, recording, comprehending, and tracking— in addition to addressing the challenges, and future applications, of this newest intelligence discipline.

This new resource presents the principles and applications in the emerging discipline of Activity-Based Intelligence (ABI). This book will define, clarify, and demystify the tradecraft of ABI by providing concise definitions, clear examples, and thoughtful discussion. Concepts, methods, technologies, and applications of ABI have been developed by and for the intelligence community and in this book you will gain an understanding of ABI principles and be able to apply them to activity based intelligence analysis. The book is intended for intelligence professionals, researchers, intelligence studies, policy makers, government staffers, and industry representatives. This book will help practicing professionals understand ABI and how it can be applied to real-world problems.

Earth observation systems, by use of space science and technology advances, present a large-scale opportunity for applying remote sensing methods with geographical information system (GIS) developments. Integrating these two methods makes it possible to achieve high-accuracy satellite data processing. This book considers aspects of GIS technology applications with space science technology and innovation approaches. It examines the potential of Earth observation satellite systems as well as existing challenges and problems in the field. Chapters cover topics such as RGB-D sensors for autonomous pothole detection, machine learning in GIS, interferometric synthetic aperture radar (InSAR) modeling, and others.

Globally, concerns for the environment and human well-being have increased as results of threats imposed by climate change and disasters, environmental degradation, pollution of natural resources, water scarcity and proliferation of slums. Finding appropriate solutions to these threats and challenges is not simple, as these are generally complex and require state-of-the-art technology to collect, measure, handle and analyse large volumes of varying data sets. However, the recent advances in sensor technology, coupled with the rapid development of computational power, have greatly enhanced our abilities to capture, store and analyse the surrounding physical environment. This book explores diverse dimensions of geo-intelligence (GI) technology in developing a computing framework for location-based, data-integrating earth observation and predictive modelling to address these issues at all levels and scales. The book provides insight into the applications of GI technology in several fields of spatial and social sciences and attempts to bridge the gap between them.

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learnLearn how companies now use location dataSet up your Python environment and install Python geospatial packagesVisualize spatial data as graphsExtract geometry from spatial dataPerform spatial regression from scratchBuild web applications which dynamically references geospatial dataWho this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.