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"Geospatial data"
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Geospatial Data, Information, and Intelligence
This book provides practitioners with structured methods for transforming geospatial data into the useful information they need to solve some of the world’s most pressing problems. It spotlights the importance of location for human experience in the everyday world and introduces spatial thinking as a foundation and the location mindset as a foundational perspective. The book starts by showing how geospatial analysis is part of a more general data-to-information refinement process that requires the right mindset, toolset, and skillset to achieve. The book then presents structured principles and practices to help geospatial analysts—whether in government or industry—improve their observational, analytical, and communication techniques. These techniques are part of an original framework for interpreting geospatial data and information: the Observe, Analyze, Communicate (OAC) Framework. The OAC framework helps practitioners at all levels break down the basic steps of their day-to-day practice and learn valuable tradecraft that they can employ during each step. You’ll learn how to center location as a foundational perspective in everyday life; use unique geospatial observation, analysis, and communication techniques; and know how to account for the role of uncertainty in assessment and production processes -- including utilizing special techniques to effectively communicate levels of certainty and uncertainty to your audience. You’ll also understand how pairing visual information with precise locational information serves to anchor human attention and provides an antidote to the common problem of disorientation. The book reveals specific techniques and tradecraft that will greatly benefit all practitioners working with visual and locational information. One such tradecraft called Structured Geospatial Observation Techniques (SGOT) includes a technique called the Four Cornerstones that will allow you to structure your approach to visual data and extract more attribute and contextual data from your object of focus. Another technique reveals industry and government-gleaned tips and tricks to creating finished geospatial communications in paragraphs, products, and presentations. Bringing together the authors’ combined 30 years of experience with geospatial intelligence (GEOINT), this book is a must-have practical resource for students, faculty, and practitioners of geospatial endeavors at any level of experience, especially fields that use imagery and spatial analysis. It serves as a textbook for classroom beginners and as a go-to desktop reference for professionals in their day-to-day geospatial efforts.
eBook
Spatial data management in apache spark: the GeoSpark perspective and beyond
The paper presents the details of designing and developing GeoSpark, which extends the core engine of Apache Spark and SparkSQL to support spatial data types, indexes, and geometrical operations at scale. The paper also gives a detailed analysis of the technical challenges and opportunities of extending Apache Spark to support state-of-the-art spatial data partitioning techniques: uniform grid, R-tree, Quad-Tree, and KDB-Tree. The paper also shows how building local spatial indexes, e.g., R-Tree or Quad-Tree, on each Spark data partition can speed up the local computation and hence decrease the overall runtime of the spatial analytics program. Furthermore, the paper introduces a comprehensive experiment analysis that surveys and experimentally evaluates the performance of running de-facto spatial operations like spatial range, spatial K-Nearest Neighbors (KNN), and spatial join queries in the Apache Spark ecosystem. Extensive experiments on real spatial datasets show that GeoSpark achieves up to two orders of magnitude faster run time performance than existing Hadoop-based systems and up to an order of magnitude faster performance than Spark-based systems.
Journal Article
IMPROVING DATA QUALITY AND MANAGEMENT FOR REMOTE SENSING ANALYSIS: USE-CASES AND EMERGING RESEARCH QUESTIONS
During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given.
Journal Article
QGIS in remote sensing set. Volume 1, QGIS and generic tools
These four volumes present innovative thematic applications implemented using the open source software QGIS. These are applications that use remote sensing over continental surfaces. The volumes detail applications of remote sensing over continental surfaces, with a first one discussing applications for agriculture. A second one presents applications for forest, a third presents applications for the continental hydrology, and finally the last volume details applications for environment and risk issues.
BOOK
Benchmarking geospatial database on Kubernetes cluster
Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.
Journal Article