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CFP: for the Handbook of Big Geospatial Data

Handbook of Big Geospatial Data

edited by 
   Martin Werner and 
   Yao-Yi Chiang 

to be published by Springer in Autumn 2019.

Aim & Scope:

Due to the advances in positioning, mobile computing, and remote sensing, very large amounts of spatiotemporal data are collected each and every day. At the same time,
applications ranging from urban computing for smarter cities over personal assistants to autonomous driving are within reach.
However, a lot of these recent advances have been made in the context of formerly isolated research domains. This leads to a situation, where the potential at the intersection
of remote sensing, GIS, big data analytics, social computing, and artificial intelligence remains difficult to unlock.

In addition to that, the sheer amount of data needs sophisticated data management and data analytics approaches, which are still in their infancy.
Furthermore, various types of spatial data lead to highly complex computational problems (e.g., trajectory computing, 3D mesh intersection, point cloud classification,
 long-term historical digital archives) limiting the exploitation of these data sources in practice.

In this context, the handbook of Big Geospatial Data aims to bring together researchers and practitioners from those various fields developing and
applying spatial computing techniques in their respective application areas. This book will foster multi-disciplinary exchange through a wide range
of topics and a large audience. This book intends to increase interdisciplinary research by forming a platform for exchanging ideas, viewpoints, and methodologies between
those various fields of spatial computing.

The book project solicits chapter proposals (1 page, 10pt) in the following or related areas covering the current methodology of a specific aspect of big
geospatial data combining accessible and introductory information with a short overview of state-of-the-art approaches:

GPS / GNSS Positioning
    Positioning in Harsh Environments
    Augmentation Systems
    Safety and Security
    Precise Positioning
Remote Sensing / Spatial Data Acquisition
    Aerial Imagery
    Laser Scanning
Special Positioning Systems
    Underwater Positioning
    Indoor Positioning
    Inertial Systems
    Visual Positioning Systems
    Integrated Navigation
Spatial Analytics
    Spatial Statistics
    Spatial Data Mining
    Trajectory Computing
    Route and Trajectory Planning
    Traffic Forecasting
    Spatiotemporal Data Modeling and Forecasting
    Spatial Artificial Intelligence
    Event Prediction
    Imagery Recognition
Open Source Data and Tools in GIS
    Data Storage
    Data Manipulation
    Data Visualization
    Data Analytics
Algorithms and Tools for Spatial Big Data
    Indexing Structures
    Data Conflation
    Streaming Data
    Semi-Structured and Unstructured Data (e.g., addresses, text documents, and micro-blogs)
    Structured Data (e.g., open city data and RDFs)
    Raster Data
    Distributed Algorithms for Spatial Data
    Ontology-based Spatial Data Integration
    Linked Data
    Spatial Big Data Infrastructure/System Architecture
    Algorithms and Tools in Geographic Information Systems
    Map Labeling
    Map Matching
    Map Conflation
    Map Generalization
    Urban Computing
    Pervasive Computing
    Smart Cities
    Smart Building
    Energy Optimization
    Digital Map Processing
    Autonomous Driving
    Cultural Heritage
    Location-based Services
Submit a 1-page (10pt, A4 or letter, PDF) chapter proposal including author information via EasyChair (

Important Dates:

Chapter Proposals Due:     Feb. 28th, 2018, any earlier is appreciated
Chapter Acceptance:          Mar. 28th, 2018
Full Chapters Due:              Oct. 15th, 2018
Notification:                         Feb. 25th, 2019

Submission Page:

After notification, accepted full chapters will be refined towards a camera-ready version with typesetting support from Springer.


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E.T.S.I. en Topografía, Geodesia y Cartografía. Campus Sur de la UPM.
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