Heterogenous Spatial Data: Fusion, Modeling, and Analysis for GIS Applications
暫譯: 異質空間數據:地理資訊系統應用的融合、建模與分析
Giuseppe Patanè, Michela Spagnuolo
- 出版商: Morgan & Claypool
- 出版日期: 2016-04-29
- 售價: $2,090
- 貴賓價: 9.5 折 $1,986
- 語言: 英文
- 頁數: 156
- 裝訂: Paperback
- ISBN: 1627054626
- ISBN-13: 9781627054621
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相關分類:
地理資訊系統 Gis
海外代購書籍(需單獨結帳)
商品描述
New data acquisition techniques are emerging and are providing fast and efficient means for multidimensional spatial data collection. Airborne LIDAR surveys, SAR satellites, stereo-photogrammetry and mobile mapping systems are increasingly used for the digital reconstruction of the environment. All these systems provide extremely high volumes of raw data, often enriched with other sensor data (e.g., beam intensity). Improving methods to process and visually analyze this massive amount of geospatial and user-generated data is crucial to increase the efficiency of organizations and to better manage societal challenges.
Within this context, this book proposes an up-to-date view of computational methods and tools for spatio-temporal data fusion, multivariate surface generation, and feature extraction, along with their main applications for surface approximation and rainfall analysis. The book is intended to attract interest from different fields, such as computer vision, computer graphics, geomatics, and remote sensing, working on the common goal of processing 3D data. To this end, it presents and compares methods that process and analyze the massive amount of geospatial data in order to support better management of societal challenges through more timely and better decision making, independent of a specific data modeling paradigm (e.g., 2D vector data, regular grids or 3D point clouds).
We also show how current research is developing from the traditional layered approach, adopted by most GIS softwares, to intelligent methods for integrating existing data sets that might contain important information on a geographical area and environmental phenomenon. These services combine traditional map-oriented visualization with fully 3D visual decision support methods and exploit semantics-oriented information (e.g., a-priori knowledge, annotations, segmentations) when processing, merging, and integrating big pre-existing data sets.
商品描述(中文翻譯)
新型數據獲取技術正在出現,並提供快速且高效的多維空間數據收集方式。空中LIDAR調查、合成孔徑雷達(SAR)衛星、立體攝影測量和移動測繪系統越來越多地用於環境的數位重建。所有這些系統提供了極高量的原始數據,通常還會與其他傳感器數據(例如,光束強度)相結合。改善處理和視覺分析這些大量地理空間和用戶生成數據的方法對於提高組織效率和更好地管理社會挑戰至關重要。
在這個背景下,本書提出了關於時空數據融合、多變量表面生成和特徵提取的計算方法和工具的最新觀點,以及它們在表面近似和降雨分析中的主要應用。本書旨在吸引來自不同領域的興趣,例如計算機視覺、計算機圖形學、地理資訊科學和遙感,這些領域共同致力於處理3D數據。為此,本書展示並比較了處理和分析大量地理空間數據的方法,以支持更好地管理社會挑戰,通過更及時和更好的決策,無論是基於特定的數據建模範式(例如,2D向量數據、規則網格或3D點雲)。
我們還展示了當前研究如何從大多數GIS軟體採用的傳統分層方法發展到智能方法,以整合可能包含地理區域和環境現象重要信息的現有數據集。這些服務結合了傳統的地圖導向可視化與完全3D的視覺決策支持方法,並在處理、合併和整合大型預先存在的數據集時,利用語義導向的信息(例如,先驗知識、註釋、分割)。