Web-based Visualization Platform for Geospatial Data

Martin Hecher

, Christoph Traxler

, Gerd Hesina

, Anton Fuhrmann

and Dieter Fellner

3,4

Fraunhofer Austria, Visual Computing, Graz, Austria

VRVis, Zentrum f

ur Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria

TU Graz, Institute of Computer Graphics and Knowledge Visualization(CGV), Graz, Austria

TU Darmstadt & Fraunhofer IGD, GRIS, Darmstadt, Germany

Keywords:

Geo-Visualization, Visual Analytics, Earth Observation, OGC, WebGL.

Abstract:

This paper describes a new platform for geospatial data analysis. The main purpose is to explore new ways to

visualize and interact with multidimensional satellite data and computed models from various Earth Observa-

tion missions. The new V-MANIP platform facilitates a multidimensional exploring approach that allows to

view the same dataset in multiple viewers at the same time to efﬁciently ﬁnd and explore interesting features

within the shown data. The platform provides visual analytics capabilities including viewers for displaying 2D

or 3D data representations, as well as for volumetric input data. Via a simple conﬁguration ﬁle the system can

be conﬁgured for different stakeholder use cases, by deﬁning desired data sources and available viewer mod-

ules. The system architecture, which will be discussed in this paper in detail, uses Open Geospatial Consortium

web service interfaces to allow an easy integration of new visualization modules. The implemented software is

based on open source libraries and uses modern web technologies to provide a platform-independent, plugin-

free user experience.

1 INTRODUCTION

In the earth observation (EO) context a variety of

sensing devices is used to collect various types of in-

formation in different formats. With the continuous

evolution of technology the resolution and thus the

amount of acquired data is increasing constantly. Be-

cause of this overwhelming data amount and hetero-

geneous data sources it is hard, e.g., to ﬁnd interest-

ing meteorological phenomenons or data correlations

between the heterogeneous datasets, especially if the

phenomenon or data correlation is not known before-

hand and cannot be searched for in an automatic way.

Tools providing efﬁcient workﬂow to search and ex-

plore within heterogeneous earth observation data are

therefore key for selecting the interesting portions of

the input data for further processing, and to skip por-

tions containing non-useful data. Secondly, compar-

ing and analyzing data from different data sources

plays an important role in the ﬁeld, e.g. when compar-

ing a computed scientiﬁc weather model output with

the actual satellite data to assess its accuracy.

In this paper we describe a web-based visualiza-

tion platform focusing on a ”Browse & Discover”

workﬂow to search through huge data repositories

from Earth Observation (EO) missions (e.g., imagery

and volume data from a satellite sensors), as well as

on a ”Compare & Analyse” workﬂow to assess data

from multiple EO sources. The development of the

platform and a reference prototype is embedded into

the research project V-MANIP (Multidimensional Vi-

sualization and Manipulation of Data). In this paper

we focus on the ”Visualization” subsystems of the

project. V-MANIP is used by scientiﬁc users such as

environmental analysts or meteorological ofﬁce per-

sonnel which require access to EO data to support

their research or decision support process. In the

project, requirements were collected to base the sys-

tem architecture on. These requirements where de-

ﬁned together with the meteorological science com-

munity. Use cases were also deﬁned to validate the

platform. A description of the use cases can be found

in Section 3, results of the validation are part of Sec-

tion 5. A demo of the V-MANIP prototype can be

found at http://demo.v-manip.eox.at.

The described platform is conceptually divided

into a frontend layer running in the web browser as

a web application, and a backend server layer. The

frontend provides the general user interface, as well

as a set of viewer components for visualizing EO

311

Hecher M., Traxler C., Hesina G., Fuhrmann A. and Fellner D..

Web-based Visualization Platform for Geospatial Data.

DOI: 10.5220/0005359503110316

In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pages 311-316

ISBN: 978-989-758-088-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

data. The backend server is responsible for prepro-

cessing the raw input data from different data sources

into formats which can be visualized at interactive

frame rates in the web application. The communi-

cation between the two layers and the internal com-

ponents within the layers are handled through Open

Geospatial Consortium (OGC) standard and proposed

standard protocols.

The web application provides a user with a set of

default graphical widgets to perform operations com-

mon for the collected use cases. Figure 1 shows the

graphical user interface of the application. A ”Layers

Selection Widget” allows to select which data sources

are displayed. A ”Timeline Widget” gives the user

visual information where on the timeline data exists

for the selected layers. It also allows the user to se-

lect a time of interest (TOI) to show data available

in the selected timespan. To select an area of inter-

est (AOI) the ”Toolbar Widget” provides different se-

lection tools, including point, line, box and polygon

selections. One main goal of the V-MANIP platform

is to provide the user with the possibility to visualize

one or multiple data sources simultaneously in dif-

ferent viewers. For that reason a ”Split View” fea-

ture allows to divide the screen into a multi-window

setup. Each of the windows can have it’s own viewer

assigned. The viewers are synchronized to show the

same spatial area and they react on events like select-

ing different layers, AOI or TOI.

Figure 1: Web application GUI with default widgets: Layer

Selection, Viewer Selection, Timeline and Toolbar.

Four viewers are provided with the system: The

2D Map Viewer, the Virtual Globe Viewer (VGV),

the Rectangular Box Viewer (RBV) and the 2D Vi-

sual Analytics Viewer. The 2D Map Viewer is not

described in detail in this paper. It provides the visu-

alization of two-dimensional map imagery and vari-

ous overlay possibilities for displaying multiple data

sources together. The Virtual Globe Viewer (VGV) is

a 3D globe for displaying maps, overlays and 3D ge-

ometry. The Rectangular Box Viewer (RBV) displays

volumetric data using image stacks. The 2D Visual

Analytics Viewer provides analytics information for a

data selection.

2 RELATED WORK

Visualization of abstract data such as sensor val-

ues in the geospatial context has become more and

more important in recent years. Dykes (Dykes et al.,

2005) provide a survey of existing systems and future

trends of geo-visualization. As one example of com-

bining geographical and multivariate visualizations,

Butkiewicz et al. draw iconic representations (called

probes) of multivariate views as detail information for

certain spots of a geographical context (Butkiewicz

et al., 2008). As another example, Brooks et al. pro-

posed a hybrid 2D/3D approach by showing multiple

information layers on top of a base terrain (Brooks

and Whalley, 2008). Zhang et al. presented a visual

analytics framework to analyse climate data. They

provide linked views of parallel coordinates with a

Google Earth plug-in, where 2D diagrams are embed-

ded (Zhang et al., 2013).

In many cases, time also plays an important role

when analyzing geographical data. Andrienko et al.

have proposed multiple approaches for visualizing

spatiotemporal patterns, including methods based on

self-organizing maps (Andrienko et al., 2010). Kapler

et al. integrate time in 2D and 2.5D terrain visual-

izations as an orthogonal axis (Kapler and Wright,

2004). In order to display more general data, Tomin-

ski (Tominski et al., 2005) proposed several 3D icons

for embedding temporal multivariate data on top of

maps. In all these cases, spatial information is pro-

vided by more or less standard maps. In contrast, we

envision a tight combination of a 3D globe with linked

interactive views providing analytic capabilities.

There are some other web-based globe viewer sys-

tems. One of them is Cesium, which is a WebGL vir-

tual globe with an integrated map engine. It allows to

watch orbits of satellites or the ISS in real-time. Ce-

sium does not support linked split views and it is not

its aim to explore Earth observation data.

OpenWebGlobe is an open source virtual globe

SDK implemented on base of HTML5, WebGL and

Javascript. The project was initiated by the Institute

of Geomatics Engineering (IVGI) of the FHNW Uni-

versity of Applied Sciences and Arts Northwestern

Switzerland (IVGI) and released as open source in

April 2011. Since then the functionality is being ex-

tended continuously. The focus lies on processing and

rendering of massive 3D geospatial environments.

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3 COVERED USE CASES

The V-MANIP project was driven by the input of the

research community to build a platform that helps sci-

entists in their daily work. For that reason real-life

use cases were deﬁned that served as validation sce-

narios for the developed software. The following list

shows deﬁned use cases, each use case shows the con-

ﬁgured web application for the scenario in a screen-

shot to give the reader an impression of the ﬂexibility

and conﬁgurability of the system.

UC1: 3D Validation of Numerical Weather pre-

diction Model Output with Satellite Data to

verify the capabilities of the system to visual-

ize, analyze and compare multi-dimensional data

coming from numerical weather prediction mod-

els and satellite system (see Figure 2).

UC2: Data Quality Control of a Meteorological

Network with Satellite Data to test the ability of

the system for monitoring a meteorological net-

work and identifying suspicious ground measure-

ments with reference to satellite and numerical

weather prediction model data (see Figure 3).

Figure 2: [UC1] 3D validation of numerical weather pre-

diction model output with satellite data.

Figure 3: [UC2] Data quality control of a meteorological

network with satellite data.

UC3: Multi-dimensional Cloud Data Analysis

aims at verifying the capabilities of the system to

visualize and analyze two-dimensional and three

dimensional data coming from space-borne plat-

forms and on ground measuring systems (weather

radar). The main scope is to verify the possibil-

ity to see combined satellite cloud structure and

precipitation data combined with ground weather

radar observations (see Figure 4).

Figure 4: [UC3] Multi-dimensional cloud data analysis.

4 SYSTEM ARCHITECTURE

The V-MANIP sofware platform allows the registra-

tion and visualization of two and three dimensional

EO data. The registration and access control for the

data through the Data Preparation Subsystem and the

Security Subsystem are not part of this description.

This paper is focused on the 3D Client-Server Sub-

system and the Visual Analytics Client-Server Sub-

system. Figure 5 shows the system architecture as

a whole, including all subsystems. Each subsystem

consists of one or multiple components. The commu-

nication between the subsystems and components is

handled via standardized or standard candidate inter-

faces from the Open Geospatical Consortium (OGC),

namely the Web Map Service (WMS) , Web Map Tile

Service (WMS) , Web 3D Service (W3DS) and the

Web Processing Service (WPS) , which are linked in

the references.

4.1 3D Client-Server Subsystem

The 3D Client-Server subsystem (3DCS) is divided

into a frontend web client and a backend server sys-

tem. The web client is running within a Javascript

capable web browser with no additional plugins in-

stalled. It provides two viewer components: the Vir-

tual Globe Viewer (VGV) and the Rectangular Box

Viewer (RBV). The two viewers are responsible for

visualizing the EO datasets. For an interactive experi-

ence when browsing and displaying EO data it has to

be streamed to the VGV and RBV viewer efﬁciently.

The architecture foresees that CPU intense prepro-

cessing of the EO data is performed on the backend

server and the result is stored in a caching system.

This allows low-latency requests for the data from the

viewers. Two caches are implemented, one for 2D

images (Tile Cache) and one for 3D mesh data (Mesh

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313

Figure 5: System architecture including all subsystems and

the interfaces between the subsystems’ components. This

paper describes a) the 3D Client-Server Subsystem includ-

ing the components colored in orange and b) the Visual An-

alytics Client-Server Subsystem including the components

colored in blue.

Cache). The preprocessing itself is done by the Fac-

tory system, which is described in Section 4.2.3 and

4.2.4. Figure 5 shows the architecture of the 3DCS

with the client and server components.

4.1.1 Virtual Globe Viewer

The web-based Virtual Globe Viewer component is

a globe application that allows a user to navigate on

the globe and to display 2D imagery data as layers,

as well as 3D geometry in a spatial context. The se-

lection tools from the Toolbox widgets allow to select

spatial areas to interact with the data, e.g. to examine

it in the 2D Visual Analytics Viewer (see 4.2.1).

The VGV is based on the open source library

GlobWeb (GlobWeb, 2014). This library was ex-

tended to visualize ”Vertical Curtains”, meaning that

a mesh comparable to a vertical plane is displayed on

the globe following the satellite track that captured

the sensor data with a certain inclination. The pre-

processed sensor data is mapped as a texture onto the

vertical mesh, forming a vertical curtain. The genera-

tion of the mesh (vertex positions, vertex normal and

texture coordinates) for a vertical curtain is done in

the Mesh Factory (see 4.2.4); the result is stored in the

Mesh Cache (see 4.1.4). Via the W3DS interface the

cached vertical mesh data is transferred to the Virtual

Globe Viewer via the OpenGL Transmission Format

(glTF, 2014), a compact, ﬁnal stage transmission for-

mat to enable rapid delivery and loading of 3D content

optimized for WebGL. The glTF format also contains

web references to the textures mapped onto the mesh.

The referenced image data is fetched by the VGV af-

ter loading the glTF data via the WMTS interface of

the Tile Cache (see 4.1.3). Figure 4 shows a vertical

curtain example on the left.

4.1.2 Rectangular Box Viewer

The Rectangular Box Viewer (RBV) is a component

for visualizing data layers containing volume data,

e.g., radar data from a satellite. In selecting an AOI

and TOI via the application widgets the correspond-

ing volume data is loaded into the viewer for an inter-

active examination.

In the V-MANIP datasets volumetric-data is or-

thogonal to the view direction of the satellite. In

the data preparation module this data is added to the

system via multi-frame TIFF ﬁles, forming an image

stack with one deﬁned axis orthogonal to the satel-

lite view direction. For the ease of description we

consider the case of a zero degree inclination of the

images (the second type in V-MANIP would be 45

◦

inclination), which means that the images are aligned

orthogonal to the vertical (with respect to the globe)

axis. The RBV will request an area of an images

stack via W3DS (where the images are contained in

a single container format), resulting in a certain num-

ber of images within the client. Image stacks for all

axes are prepared on the client side. A second visu-

alization option is to render the images in the stack

blended over each other (depending on the view di-

rection). The base software library for the RBV is the

XTK library (The X Toolkit (XTK), 2014).

4.1.3 Tile Cache

The Tile Cache component provides a cache of over-

lay images from V-MANIP layers as well as textures

to be applied to vertical curtains. The beneﬁts of us-

ing a cache is that the on-the-ﬂy production of the tiles

may be too time-demanding for a responsive user ex-

perience. A pre-seeded cache simply has to return

the requested tiles with little or no overhead regarding

the processing time for data creation. The data stored

in the cache is requested from the Mesh Factory and

stored in an WMTS compatible database structure.

The cached data can be requested by the web appli-

cation’s viewer modules via the WMTS protocol.

4.1.4 Mesh Cache

The Mesh Cache is located in the server backend and

stores all mesh information necessary to display ver-

tical curtains. The stored data consists of vertex po-

sition, vertex normals and texture coordinates to map

pre-processed texture images - which contain the ac-

tual EO information - onto the mesh. When the web

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application requests a speciﬁc vertical curtain tile via

the W3DS interface the cache checks if the tile is al-

ready created. If not, the data is requested from the

Mesh Factory component, which creates the mesh in-

formation for the vertical curtain. The main purpose

of this cache is to enable fast data transmission to a

client. To ensure a reasonable transmission time to a

request it is necessary to keep the size of the trans-

mitted data at a minimum. This is taken care of in

using the glTF format as output format of the Mesh

Factory. glTF includes mesh compression algorithms

for minimizing the transmission size.

4.2 Visual Analytics Client-Server

Subsystem

The Visual Analytics Subsystem consists of compo-

nents to enable an interactive, visual analysis of Earth

observation data. It applies methods of information

visualization for in-depth visual exploration of com-

plex data and its relations. These components are de-

scribed in the following sub-sections.

4.2.1 Visual Analytics Viewer

The Visual Analytics Viewer provides different in-

formation visualization methods for interactive visual

analysis of Earth observation data. This allows de-

tailed investigation of selected data sets. Complex

data sets and their relations are intuitively compre-

hensible. It makes outliers and clusters easily percep-

tible. This subsystem is based on the Javascript li-

brary Data Driven Documents (D3) (Bostock, 2014).

A set of different interactive views has been selected

based on use cases, which are a box plot for statistical

analysis, a scatter plot showing data distribution, par-

allel coordinates for relations of multivariate values

and streamlines for direct visual comparison of time

series. It is embedded in a multi-view web client and

thus preservers the geo-spatial context.

4.2.2 Visualization Data Factory

The Visualization Data Factory is a component lo-

cated on the server backend and services various pro-

cesses through WPS. Requesting the execution of a

speciﬁc process with the expected parameters will re-

turn a result that can then be visualized by the 2D

Analytics Viewer component. The Visualization Data

Factory can access registered data directly as well as

execute various processes which return processing re-

sults.

4.2.3 Tile Factory

The Tile Factory generates data requested by the Tile

Cache (see 4.1.4) and serves it through WMS. Vol-

umetric data is represented by a stack of GeoTIFF

layers (Sazid Mahammad and Ramakrishnan, 2014).

Each layer represents a volumetric slice parallel to the

ground. This stack is converted by the Tile Factory in

an volumetric representation encoded in the NIfTI-1

data format (National Institutes of Health, 2014). The

Tile Cache requests volumetric data within the bounds

of a single tile from the Tile Factory, which crops and

merges the GeoTiff layers accordingly.

4.2.4 Mesh Factory

The Mesh Factory generates the 3D mesh and texture

data needed by the Virtual Globe Viewer, which re-

quests it via the Mesh Cache. The data is provided

to the Mesh Cache through the GetScene request, as

speciﬁed in the W3DS standard.

The Mesh Factory requests vertical curtain data

information from a database on the ﬁle system, which

was populated by the Data Preparation Subsystem.

This data is delivered as XML encoded sample points

along the satellite footprint path and height values,

and GeoTIFF (Sazid Mahammad and Ramakrishnan,

2014) images containing the actual measurements on

the vertical grid speciﬁed by these sample points.

These samples occur in continuous strips along the

satellites path on the ground. The Mesh factory has

to crop and merge them to the bounds of the tile re-

quested by the Mesh Cache. Special care has to be

taken to cover all edge cases that occur when clipping

geometric data on a spherical topology: wraparound

of coordinates over the 180

◦

meridian, singularities on

the poles and paths coinciding with tile borders. The

processed geometry representing the vertical satellite

measurements over time result in a dense geometric

mesh. To reduce the rendering load for the viewer, es-

pecially when many curtains are visible, the geometry

can be requested in several levels of detail. Both mesh

and textures are reduced, resulting in approximately

the same amount of data for all tile sizes. The mesh is

delivered as compressed binary data in the glTF for-

mat. The format is currently in the OGC’s standard

candidate phase and will in our opinion future-proof

our interface.

5 CONCLUSION

The target science communities were included in

a continuous evaluation process during the project.

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315

They expressed a clear interest in the results an even

the desire to incorporate V-MANIP into some of their

platforms. Interactive tutorials for every use case al-

lowed new users to become easily acquainted with the

software.

Some of the functionalities considered and re-

ported as highlights by the validation group members

are:

• Web application approach: No need for installing

or updating software on their local machine.

• Performance of the system: Speed of data access

and data manipulation.

• Time slider: Allows easy interpretation of where

and when data is available.

• Several illustration facilities: 2D views on

map/virtual globe, 3D views both in space (x,y,z)

and space/time (x,y,t) and analytical tools to dis-

play time series at selected points.

• The visualization of temporal and spatial avail-

ability of different data sources and the possibility

to combine different data sources in one applica-

tion which is essential for data viewing and data

mining with regard to big data.

Future work topics collected from the validation

group were:

• Additional information of the visualized data,

such as recording mission or sensor type and its

processing pipeline should be displayed, as well

as what algorithms were applied or which entities

did process the data.

• Upload of local data: Provide an interface to allow

direct upload of local data (e.g. data owned by

an institution) to be integrated into the system for

visualization.

In collaboration with science communities new vi-

sualizations and algorithms can be deﬁned extending

the functionality of the open-source platform.

ACKNOWLEDGEMENTS

The V-MANIP project was funded by the European

Space Agency in Frascati, Italy (ESA/ESRIN) under

the reference AO/1-7193/12/I-AM. We would like to

thank the project team partners EOX (project lead),

Berner & Mattner, SISTEMA and ZAMG.

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