Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancem...Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.展开更多
Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic in...Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic information systems(GIS)may open new opportunities for data scientists.In theory,analysts may simply ask spatial questions to exploit diverse geographic information resources,without a need to know how GIS tools and geodata sets interoperate.In this outlook article,we investigate the scientific challenges of geo-analytical question answering,introducing the problems of unknown answers and indirect QA.Furthermore,we argue why core concepts of spatial information play an important role in addressing this challenge,enabling us to describe analytic potentials,and to compose spatial questions and workflows for generating answers.展开更多
文摘Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.
基金supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement no.803498(QuAnGIS)).
文摘Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic information systems(GIS)may open new opportunities for data scientists.In theory,analysts may simply ask spatial questions to exploit diverse geographic information resources,without a need to know how GIS tools and geodata sets interoperate.In this outlook article,we investigate the scientific challenges of geo-analytical question answering,introducing the problems of unknown answers and indirect QA.Furthermore,we argue why core concepts of spatial information play an important role in addressing this challenge,enabling us to describe analytic potentials,and to compose spatial questions and workflows for generating answers.
