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A Semantic Geodatabase for Environment Analysis. Extraction, Management and Sharing of Earth and Water Information in GIS


Abstract and Figures

The great potential of GIS to manage and analyse georeferenced information is well-known. The last several years of development of ICT (Information and Communication Technologies) saw a necessity of interoperability arise, from which the Semantic web standards and domain ontologies are derived. Specific application field ontologies are often insufficient for representing the information of multidisciplinary projects. Moreover, they are often aimed at the representation of homogeneous data formats (alphanumeric data, vector spatial data, raster spatial data, etc.). In this scenario, traditional GIS often have a limit: they implement personal data models, which are very difficult to exchange through different systems. In this study we structured a GIS for the monitoring project ALCOTRA ALIRHYS according to parts of two different self-integrated ontologies, from the perspective of the major interoperability of the system and the sharing of data through a web-GIS platform. The two standard models chosen (SWEET ontology and INSPIRE UML model) have been integrated in a unique conceptual model useful both for geometric and cartographic data, and for thematic information. In this case, the implemented schemas are published on the project website, and are available for other users who want to produce similar studies. Since user-friendly results were desirable, some integrated commercial widespread software programs have been used even if their abilities to manage such a GIS are suboptimal.
Content may be subject to copyright.
A Semantic G
eodatabase for
Extraction, Management and Sharing of Earth and Water Information in GIS
, Francesca Noardo
Politecnico di Torino - DIATI, Duca degli Abruzzi, 24 – 10129 Torino, Italy
{andrea.lingua, francesca.noardo}
Keywords: Geographic Information System, Geoprocessing, Ontology, Digital maps, Geodatabase.
Abstract: The great potential of GIS to manage and analyse georeferenced information is well-known. The last several
years of development of ICT (Information and Communication Technologies) saw a necessity of
interoperability arise, from which the Semantic web standards and domain ontologies are derived. Specific
application field ontologies are often insufficient for representing the information of multidisciplinary
projects. Moreover, they are often aimed at the representation of homogeneous data formats (alphanumeric
data, vector spatial data, raster spatial data, etc.). In this scenario, traditional GIS often have a limit: they
implement personal data models, which are very difficult to exchange through different systems. In this study
we structured a GIS for the monitoring project ALCOTRA ALIRHYS according to parts of two different self-
integrated ontologies, from the perspective of the major interoperability of the system and the sharing of data
through a web-GIS platform. The two standard models chosen (SWEET ontology and INSPIRE UML model)
have been integrated in a unique conceptual model useful both for geometric and cartographic data, and for
thematic information. In this case, the implemented schemas are published on the project website, and are
available for other users who want to produce similar studies. Since user-friendly results were desirable, some
integrated commercial widespread software programs have been used even if their abilities to manage such a
GIS are suboptimal.
Traditionally, Geographic Information Systems
(GIS) are powerful instruments for storing
geospatial data in digital archives and managing
these data for inferring further knowledge,
extracting spatial information and realizing
geometric and morphological analysis. Another
basic capability of GIS is the archiving of dynamic
data related to monitored values in a “many to one”
relation with reference to spatial objects. These can
be queried and represented on the land, with
additional consideration of the time at which the
data have been acquired (the 4
dimension managed
by GIS).
Since the mid-1990s some research has further
developed these systems. In particular, a need for
interoperability arose; the solution was found in
ontologies, in order to better manage the semantic
content of spatial data (Freksa, Barkowsky, 1996,
Fonseca et al., 2002), and in object-oriented GIS
(Scholl, Voisard, 1992), which permitted the
implementation of some important characteristics
of the semantic structure (Mennis, 2003). Today,
the achievement of the evolution of the World Wide
Web, the Semantic Web, offers theories and
structures that make the theorized interoperability a
reality (Waters et al., 2009). Standard languages and
services foster this aim: OGC (Open Geospatial
Consortium) published standards for spatial data
exchange, such as WMS (Web Map Service), WFS
(Web Feature Service), WCS (Web Coverage
Service), WPS (Web Processing Services) and so
on. Moreover, the semantic web offers more
standards and tools aimed at reaching maximum
data integration and exchangeability. Structuring
data by means of these tools offers an enhanced
ability to run automated reasoning and evolved
queries on published spatial data. In several fields
of application some standard ontologies have been
developed, as references for the management of the
semantic content of represented data (Pundt, Bishr,
2002). However, these ontologies often seek to
represent alphanumeric data, without concern for
spatial implications. In the meantime, other
ontologies, such as CityGML (OGC, 2008) or
INSPIRE UML (Unified Modelling Language)
model (
model/approved/r4618-ir/html/), consider the
cartography, but are insufficiently able to manage
the data of particular application fields in a GIS. For
this reason the need for ontology integration arises.
In this paper, the environmental data (geometric
and dynamic data) studied during the European
project ALCOTRA ALIRHYS have been managed
in a GIS and in an external database structured
according to a data model designed by integrating
existing ontologies. The project aims to study
subterranean water resources through several
monitoring activities ( In this
way the data produced are codified to be uniquely
interpreted, and to be effectively shareable on the
web without losing part of their meaning. Moreover,
when a GIS or a database is constructed in the
traditional way, the conceptual model used is
shared, together with other metadata, in order to
limit interpretation ambiguity. However, sharing of
implemented structures is not common. In this
project, both the models and the XML schema of the
constructed geodatabase in ESRI ArcGIS and the
related database tables in MS Access are published
on the project's website ( in
order to foster the reproduction and the reuse of the
method for similar studies, even in a stand-alone
The advantages of data management in GIS
have been exploited by running different analyses
on the data archived in order to acquire further
information, useful for the monitoring of springs;
this work is described in the final part of this paper.
1.1 Semantic data management:
ontologies and standards
Representation in many fields of application has
been influenced in recent years by new paradigms
established by the web and informatics
technologies. The Semantic Web is intended to
realize a common framework that allows the
meaningful content of data to be shared, beyond the
boundaries of applications, and to be easily reused
for automatic processing. Permitting an effective
semantic interoperability, documents are associated
to metadata that specify the semantic context in
standardized language, and data are structured
according to pre-formatted and known data models.
One of the most powerful tools for reaching these
objectives is the formulation of domain ontologies,
primary conceptual models that provide completely
solution-independent schemas in order to support
modelling in producing suitable representation
structures. A set of standard languages is available
for defining interoperable ontologies
(http://www.w3. org/). Almost all these languages
have as their origin structure the eXtensible Markup
Language (XML), which is then specified in a set of
more evolved structures developed to better express
the logic structure and correcting semantics of data.
W3C (World Wide Web Consortium) is the
organization developing the structures to enable the
Semantic web. It published some specific languages
to model the data in domain ontologies, in order for
the data to be effectively shared. These evolve from
the original RDF (Resource Description
Framework) ( to more
suitable ones for representing knowledge structures:
OWL (Ontology Web Language)
(, and some evolutions
of it (OWL 2, DAML+OIL, OWL1 DL). These
languages are best suited for representing
information in the form of alpha-numeric data, but
they have some lacks in the suitable management of
geometric information.
In this scenario, many domain ontologies are
being developed. Among these, we find the NASA
(National Aeronautics and Space Administration)
SWEET (Semantic Web for Earth and
Environmental Terminology) ontology, intended
for the representation of Earth and Environmental
Sciences (EES) (Raskin, 2004). This ontology is in
modelled in the OWL language, and includes a very
high variety of concepts about both the object
studied and the methodology aspects (research,
analysis, measurements, residuals, etc.); these could
be effectively transposed to other application fields.
SWEET has become the de facto standard for data
management in EES (Di Giuseppe, 2014).
Concurrently, some particular formal languages
have been developed for spatial data. The most
widespread of these are published by the OGC
(Open Geospatial Consortium), an organization
founded to develop publicly available geo-enabled
standards. In particular, it encoded GML
(Geography Markup Language, ISO 19100 series of
International Standards and OpenGIS Abstract
Specification), which is an XML schema for the
description of the application, transport and storage
of geographic information. This has been applied to
the formulation of some standard models - such as
CityGML, the common semantic information
model for the representation of 3D urban objects –
and allows these data to be shared over different
applications. Another fundamental application of
GML is the INSPIRE conceptual model
( /data-
model/draft/r4530/), which is the pre-specified
ontology for formulating harmonic and
homogeneous maps in the European Union.
A point of contact between these two fields can
be found in considering the W3C language
SPARQL (SPARQL Protocol and RDF Query
Language) (
query/), a query language for RDF structured data.
Starting from this language, OGC developed the
geoSPARQL language
ql); it defined a vocabulary for representing
geospatial data in RDF, as well as an extension to
SPARQL for processing geospatial data. This
language offers the ability to transition between the
languages GML and RDFS/OWL. In the future, this
will provide a good solution for managing and
sharing data, as some research has already outlined
(Tschirner, 2011).
In this study, the available entities have been
extracted from the SWEET ontology in order to
model the environmental objects studied during the
project ALIRHYS. Concurrently, the spatial
properties of objects have been managed by
integrating the selected part of the SWEET ontology
with the entities extracted from the INSPIRE UML
model, to complete the representation for suitable
management in the GIS.
The INSPIRE Directive
( is a European
Union directive aimed at creating a European spatial
data infrastructure, able to establish a unique
network for sharing environmental spatial
information among European nations. The directive
has been in force since 15th May 2007, and after
passing through various stages will be fully
implemented by 2019.
Two of the main principles of INSPIRE are: “It
should be possible to combine seamless spatial
information from different sources across Europe
and share it with many users and applications; […it
should be] easy to find what geographic information
is available, how it can be used to meet a particular
need, and under which conditions it can be acquired
and used.” (INSPIRE, 2007) As in some other
works related to cartography harmonisation (Tóth
2007, Afflerbach 2004) and also in the ALIRHYS
project, our purpose was to respect these two
principles by producing some cartographic data and
sharing them on a WebGIS together with every
useful metadata file.
Figure 1: Study Area of the project ALIRHYS.
The European project ALCOTRA – ALIRHYS
(2013-2015) has been developed by the partnership
among the Politecnico di Torino, Politech Nice
Sophia, Université Nice Sophia - Antipolis,
Regione Piemonte and the Nice - Côte d’Azur
Metropole, with the aim of studying and monitoring
the subterranean hydric resources in the cross-
border mountainous territory between the provinces
of Cuneo and Nice (Fig.1). These activities are
required in light of climatic changes that have
occurred over the last several years and that increase
the risks caused by climate variability: periods of
drought alternated with flooding are becoming more
and more frequent. Subterranean hydric resources
feed several springs in the territory from which the
local hydrographic network originates, and they
also feed several aqueducts that supply drinking
water to users. For this reason, it is essential to know
the chemical composition of the water and to
foresee the influence of rainfall and snow fusion on
the behaviour of the springs in order to optimize
their management (Bellone et al., 2014). These
parameters can only be studied from a cross-border
point of view as the geological assets influence both
sides of the Alps. Therefore, studies must be carried
out in a comprehensive manner. Furthermore, this
cross-boundary case study justifies the use of
standards for the management of the project’s data;
these standards ensure that data can be shared,
queried and used in analysis.
In this article, the part of the project regarding the
management, analysis and integration of data in GIS
is presented.
The data of the springs monitoring project
ALIRHYS have been managed in a GIS. As usual,
a conceptual schema for modelling the data is used,
but particular care was taken in choosing entities
useful for the specific needs of the study and
appropriate for the two reference ontologies: the
SWEET ontology and the INSPIRE UML model.
The resulting conceptual model is shown in Figure
2. It shows the entities present in the INSPIRE
conceptual model (in blue), which have been used
mainly for the harmonisation of digital maps
(Noardo et al., 2015); the entities extracted from the
SWEET ontology (bordered in red) are used to
manage the remaining concepts present in the
system. These last ones and some additional entities
are added because the needs of the project are in turn
divided in spatial entities (in yellow), dynamic data
tables (in violet), and geoprocessing products (in
pink). The integration between the two ontologies is
indicated by the green arrows.
In the implementation, different logic data
models are useful for different data management
requirements. It would be suitable to manage spatial
data in an object-oriented database, as is also
implicit in the structure of standard models. This
kind of logic model enables some meaningful
constructs for cartographic objects, such as
polymorphism and inheritance (Worboys, 2004).
On the other hand, reams of dynamic data can be
managed effectively in a relational database, in
which they can be automatically imported and
suitably queried. Software implementing the hybrid
model, an “object-relational database management
system” (ORDMBS), could have good performance
in both cases (examples of these are PostgreSQL
and Oracle). However, these system are often less
widespread than others that use a relational database
model, such as ESRI ArcGIS or MS Access; these
are more commonly known and, consequently,
more user-friendly. In this situation ease of use is
important, so these last two software programs have
been chosen to permit an easy re-employment of the
data and of the schemas implemented by without
advanced qualifications.
Since the implementation with this kind of
software is not the most ideal, the model has been
divided in three main parts. The first segment
concerns the harmonisation of digital maps (ESRI
ArcGIS); the second manages the representation of
useful georeferenced data of the project (ESRI
ArcGIS), and the third deals with dynamic data
tables, which are managed in an external DBMS
(Database management system), MS Access.
Ontologies and data models are oriented
towards the publication of data on the web and the
sharing of these data through specific web service
interfaces. During this project the aim was to
conduct some analysis on the springs and on the
studied area, even in a stand-alone environment.
Only in a final part of the project have results been
published in a WebGIS using the open-source
platform Geonode. The requirement of respecting
defined data models was useful for obtaining
structured data in this case; it could potentially be
implemented in other similar systems, or shared on
the web to enhance the system’s functionality for
future work.
3.1 The ALIRHYS geodata
3.1.1 The harmonisation of digital maps
As a first step in building a geographic database
for the project map, GIS tools were used to
harmonize the available national cartographic
products by exploiting both their geoprocessing
capabilities and their database characteristics (as
better explained in Noardo et al., 2015). In this
paper we focus on the part of the harmonisation
processes concerning the digital maps. Analysing
the national digital maps, one notices some
differences in the geometric visualisation of objects
due to the origin of the data, the plotting methods,
and the map’s nominal scale. These geometric
differences are too difficult to solve, because doing
so would require the re-plotting of maps or the
acquisition of new homogenous data for the whole
area; this is not within the scope of this project.
What is interesting in this context are the data
structures, which have been analysed and
transformed in order to make them harmonic. To
pursue this aim the part of the defined conceptual
model including INSPIRE entities has been used as
a reference.
The national databases were analysed in order to
extract a simplified version of the conceptual model
structuring the maps, considering only entities
useful for our representation needs. The next step
was the mapping of each entity into the selected part
of the INSPIRE model, using a transformation to
make the data homogeneous (Noardo et al., 2015).
3.1.2 The construction of an interoperable
geodatabase for ALIRHYS data
On the harmonised base maps some original
data were produced. These have been mainly
structured following the part of the model designed
on the SWEET ontology. Entities with spatial
consistency have been archived as feature classes in
a geodatabase, while the entities mainly represented
by dynamic data tables are managed in a related
external database.
Using ESRI tools, a geodatabase is created in
ArcGIS. This is useful in order to define the static
entities of the system, to establish relationships and
topological rules between them and to impose
constraints including taxonomies and subtype
definition (tables 1-3).
Table 1: entities (feature classes) of the geodatabase
Springs Points
MeteoStations Points
SamplingPoints Points
InputTrackingPoints Points
Stream Points
LandRegion with
HydrogeologicalProperties Polygon
DrainageBasin Polygon
Table 2: Relationship classes of the geodatabase
S_Comparison_MS Relation Springs -
1-n relation
S_Comparison_SP Relation Springs -
n-n relation
S_Comparison_SS Relation Springs - Streams
n-1 relation
Figure 2 - Conceptual reference model used for the GIS.
Through these associations it is possible to
query and access the data tables in a cross-
referenced way for easy comparison.
Table 3: Topology rules stated in the geodatabase
SamplingPoints Must be Properly Inside -
Spring Must be Properly Inside –
The external database is defined in MS Access
for the representation of the dynamic entities in
Table 4 (for which extensive records are needed).
These are represented in violet in the reference
conceptual model. Once the tables are related to the
geodatabase feature classes, they can be queried; it
is then possible to carry out analysis and statistics
on them while also considering their spatial
reference (Fig.3). The updating of the tables in the
external software is automatically translated to the
Figure 3 – Example of resulting GIS with related dynamic
Some difficulty is encountered in the limited
compatibility of the two software (ESRI ArcMap
and MsAccess) in their most recent versions, and by
the limitations on data recording for memory
occupation reasons. Therefore, in subsequent
projects we will use open-source platforms to work
towards general interoperability and better
The data are then exported and published on a
WebGIS, which can be accessed from for viewing the data;
downloading is available through the Web Map
Service (WMS), Web Coverage Service (WCS) and
Web Feature Service (WFS). Since the data are
semantically structured in a known and shared
model, it will be easier to correctly interpret them
On the other hand, the implemented structures
can be extracted from both software programs as an
XML file (with or without data) and can be shared
and easily imported by other users in the same
software for obtaining similar researches and
comparable data. The XML files are also shared on
the project web site to facilitate the use of the same
Table 4: Dynamic data tables stored in the external DB:
DailyMeteoParameters (Row data)
HourMeteoParameters (Row data)
MonitoringParameterValues (Row data) on Springs
IsotopesParametersValues on SamplingPoints
IsotopesParametersValues on SamplingPoints [Snow]
BaseChemicalQualityParametersValues on
SamplingPoints [LiquidWater]
BaseChemicalQualityParametersValues on
SamplingPoints [Snow]
IsotopesParameterValues on Springs [LiquidWater]
BaseChemicalQualityParametersValues on Springs
MonitoringParameterValues (Row data) on Streams
TrackingTestsData (Row data)
TrackingParametersValues (Level 2 of processing)
3.2 Geoprocessing tools of GIS for
subterranean resources analysis
The other important capability of GIS is
represented by the geoprocessing algorithms, which
can be used for analysing terrain models in order to
extract important information for the interpretation
of the ground, and, in this case, for the study of
water resources.
This capability has been used for the automatic
extraction of morphologic maps for the study of
flows. Moreover, the extracted maps together with
some data derived from satellite imagery have been
used for estimating the snow volume in the study
area in particular periods. This can be useful in
order to manage the springs’ water by foreseeing the
possible flow rate in the snow fusion period.
The algorithms implemented in GIS
management software are able to extract a number
of morphologic and hydrologic data, starting
primarily from the DTM. This can be extremely
useful when discussing the geological aspects and
water flows. In Figures 4-6 there are some examples
of information extracted by a simple DTM. The
processed objects correspond to the pink entities of
the conceptual model.
Figure 4: Morphometric parameters.
Figure 5: Catchment computed with the DTM as input file
(in black). The stream’s location appears, as visible from
the comparison with its complement in the digital map (in
Figure 6: Sink watershed, allowing for the isolation and
visualization of each basin for conducting more specific
analysis on the hydric resources (tracking test hypotheses,
rain and snow monitoring, pollution source monitoring,
Some of these maps, such as the contour, aspect and
slope maps, are simply extracted by the DTM; other
algorithms analyse these basic maps to produce
modelling maps of flow dynamics that permit
researchers to locate the rivers, basins and other
such features. This can help in the hydrologic and
morphologic analysis. Another interesting tool
permits the calculation of groundwater flow,
starting from the DTM, the surface model of the
aquifer, and some other data. This could an
interesting avenue of future work linked to this
Modelling the conceptual schema of a GIS using
affirmed ontology makes the information less
ambiguous, and data could be more easily sharable
on the web in line with the goals of the Semantic
Web. Using the integration of different application
field ontologies makes it possible to exhaustively
represent the environmental information, which
traditionally has been an enormous part of
documentation and monitoring data; this data is
inherently related to the Earth’s surface, a geometric
The GIS conceptual model includes several
kinds of data: geometric vector data, dynamic data
and raster datasets. It is the precondition for real
integrated management of all these kinds of data,
even if in this paper this work is still quite
incomplete. Future goals will include the automatic
implementation of such structures to achieve a
unique framework for data sharing. Some limits of
the system built are surely in the software used,
because these programs do not include some
essential characteristics of the ontological models.
For example, an ORDBMS (Object-Relational
Database Management System) such as
PostgreSQL could better manage both the
geometric and the dynamic data by also using some
object-oriented properties that are extremely useful
for expressing the object’s structure.
Such approaches could be worse for the
usability by inexpert users. This problem would be
solved by managing the data directly on the web,
through some user-friendly interfaces, even with a
greater requirement for informatics contribution.
On the other hand, this could effectively be a step
towards the realization of the Semantic Web’s aims
of sharing structured data and processing services.
Moreover, a limit to interoperability is the use
of commercial software, chosen for the user-
friendly interfaces and widespread adoption; these
programs work mainly with their own formats. In
subsequent studies, this issue will be solved through
the use of open source and self-integrated software.
Furthermore, it is well-known that GIS data
management is used for the geoprocessing and data-
modelling of archived data, in particular for
environment applications. The relationship between
GIS and environmental modelling has been studied
from a semantic point of view as well (Fallahi et al.,
2008, Kiehle et al., 2006, Argent, 2004). This will
be object of future in-depth analysis, with an
ultimate goal of a truly complete and integrated
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... The Model MAP is applied to a hydrometeorological research infrastructure, after being compared to the component-based water resource model ontology. Lingua and Noardo (2015) structure a GIS-based on parts of two different self-integrated ontologies from the viewpoint that the system plays a major role in interoperability and data sharing through a web-GIS platform for the extraction, management, and sharing of earth and water information. Essawy et al. (2017) perform the Variable Infiltration Capacity (VIC) hydrologic model to evaluate the OntoSoft (Gil et al., 2015) ontology for describing metadata for scientific software. ...
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Big data generated by remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and a wide range of structured and unstructured sources necessitates significant data and knowledge management efforts. Innovations and developments in information technology over the last couple of decades have made data and knowledge management possible for an insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that led to new ideas in scientific research and the business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. A systematic literature review was conducted to examine research involving ontologies related to hydrological processes and water resource management. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle's complex structure, as well as the predictions of its processes. They contribute to the development of ontology-based information and decision support systems; understanding of environmental and atmospheric phenomena; development of climate and water resiliency concepts; creation of educational tools with artificial intelligence; and strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide the development of next generation artificial intelligence applications. The study also discusses future research prospects in combination with artificial intelligence and hydroscience.
Due to the emergence of digital revolution and competitiveness in recent decades, almost all organizations and industries intend to develop solutions to extract information from unstructured documents. These documents comprise of information related to multiple divergent domains and therefore there is a need of a multi-domain knowledge base. Since recent research works suggest ontology as the predominant model, it is proposed to evolve a unified ontology modeling approach with multiple layers and divergent domains to support information processing from unstructured documents. The model is evolved by integrating relevant domains to facilitate cross domain query. Further as the features of unstructured documents span across multiple domains, domain identification is to be performed prior to any information processing. Hence, an attempt is made to identify the domain using the proposed ontology model. The proposed ontology is developed for the Thermal Power Plant Industry and domain identification is demonstrated with an example. A statistical similarity index is proposed to associate divergent volatile features of unstructured text with ontology knowledge for domain identification. The outcome of the proposal is evaluated using the proposed similarity index. A subsequent study to extract information using classified content with the support of Directed Acyclic Graph relationship is under progress. The merit of the proposal is its ability to extend its usage across multiple stages of information processing with distinctive purpose.
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An essential support for environmental monitoring activities is a rigorous definition of a homogeneous cartographic system, required to correctly georeference and analyse the acquired data. Furthermore, since 2007, the European Infrastructure for Spatial Information in the European Community (INSPIRE) Directive affirms the necessity to harmonise the European maps for permitting cross-border analysis. For satisfying these requirements, the authors have developed a procedure for the cartographic harmonisation in the cross-border area studied during the European project Alpes Latines-Coopération Transfrontalière (ALCOTRA)–Alpes Latines-Individuation Resources Hydriques Souterraines (ALIRHyS). It concerns the hydrogeological study of various springs and other water resources in an area between Italy and France including their constitution in a cross-border system. The basic cartographic information is obtained from existing national maps (Italian and French data), which use different coordinate systems or projection methods and are produced from different data acquisitions and processes. In this paper, the authors describe the methods used to obtain well-harmonised middle-scale maps (aerial orthophotos, digital terrain model and digital maps). The processing has been performed using geographic information system (GIS) solutions or image analysis software in order to obtain useful and correct cartographic support for the monitoring data, even if the obtained maps could be further analysed or refined in future works.
Conference Paper
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The traditional workflows in geography and cartography have been redefined by the change in the production paradigm. From the single purpose data collection the focus has been shifted to information management; to the establishment of spatial data infrastructures (SDI) at local, national, regional and global levels. The concept for establishing the European SDI emerged from the need of the environment, where GIS give an efficient framework for data processing together with effective communication tool for representing information. The INSPIRE Directive of the European Commission, which has been agreed upon by the European Parliament and the Council will enforce better and wider use of the data and the interoperability between the systems operated by the Member States. In order to bring the initiative to success the provisions for the implementation will be based on the consensus of the participants. Five working groups, called Drafting Teams, are working on the aspects of metadata, data harmonisation, network services, data sharing and implementation monitoring. How do the traditions and the emerging technology interact in case of SDI and cartography? By its nature SDI is a much wider notion. Never the less, the experience of cartography directly contributes, amongst others, to the following aspects of SDI: 1. Spatial cognition, data harmonisation In spite that SDIs are usually defined within service oriented architecture, certain data harmonisation work is usually needed, which takes place at semantic, schema, data and information product level. Cartography has accumulated experience in describing and classifying the Universe with consistent models and in coherent channelling information to the users. 2. Level of details, scale and data quality Experience in coupling different levels of aggregation with reasonable spatial resolution (scale) and meaningful data quality requirements is another asset of cartography. 3. Multiple representation, data consistency A natural requirement in SDI is that objects represented at different level of details are consistent. Multiple-representation is widely researched and practiced in digital cartography. 4. Portrayal Portrayal plays an important role in discovery and viewing services, but also in communication of the spatially enabled information. Clear legend, adaptive zooming with the appropriate multiple-representation and generalisation capabilities in the background greatly facilitate this task. The presentation and the full paper will explain how the above fields may contribute to SDI building at European level, based on the requirements of the INSPIRE Directive.
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Scientists in the Earth and Environmental Sciences (EES) domain increasingly use ontologies to analyze and integrate their data. For example, the NASA’s SWEET ontologies (Semantic Web for Earth and Environmental Terminology) have become the de facto standard ontologies to represent the EES domain formally (Raskin 2010). Now we must develop principled ways both to evaluate existing ontologies and to ascertain their quality in a quantitative manner. Existing literature describes many potential quality metrics for ontologies. Among these metrics is the coverage metric, which approximates the relevancy of an ontology to a corpus (Yao et al. (PLoS Comput Biol 7(1):e1001055+, 2011)). This paper has three primary contributions to the EES domain: (1) we present an investigation of the applicability of existing coverage techniques for the EES domain; (2) we present a novel expansion of existing techniques that uses thesauri to generate equivalence and subclass axioms automatically; and (3) we present an experiment to establish an upper-bound coverage expectation for the SWEET ontologies against real-world EES corpora from DataONE (Michener et al. (Ecol Inform 11:5–15, 2012)), and a corpus designed from research articles to specifically match the topics covered by the SWEET ontologies. This initial evaluation suggests that the SWEET ontology can accurately represent real corpora within the EES domain.
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The project GiMoDig (Geospatial Info-Mobility Service by Real-Time Data Integration and Generalisation) started in November 2001 and is funded by the European Union. With a duration of 3 years this EU-project has the goal to develop methods for harmonisation, generalisation and visualisation of national topographic data sets for mobile users in real time. The project partners are the Finnish Geodetic Institute as a project coordinator, the National Mapping Agencies (NMAs) of Denmark, Finland, Sweden and Germany and the University of Hanover, the Institute of Cartography and Geoinformatics. One of the tasks in the GiMoDig project is to define a Global Schema for the core national topographic data sets. For this purpose an inventory on the national databases is prepared to list the differences in data availability and data modelling. Based on that inventory, a selection of feature types suitable for Location Based Services (LBS) is made. The idea is to use the least common denominator as selection criteria but this subset already lacks some important feature types. Finally all features that are supported by a majority of national data sets are integrated in the Global Schema. The Global Schema is defined with a detailed description about feature type, attributes, collection criteria and geometry type. All necessary information about harmonisation operations are given to be able to transform the topographic data from the national schema into the Global Schema. Further scrutinizations of test data lead to an improvement and adaptation of the Global Schema.
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Distribution of snow water equivalence (SWE) was measured in the Emerald Lake watershed located in Sequoia National Park, California, by taking hundreds of depth measurements and density profiles at six locations during the 1986, 1987 and 1988 water years. A stratified sampling scheme was evaluated by identifying and mapping zones of similar snow properties on the basis of topographic parameters that account for variations in both accumulation and ablation. Elevation, slope, and radiation values calculated from a digital elevation model were used to determine the zones. Of the variables studied, net radiation was of primary importance. Field measurements of SWE were combined with the physical attributes of the watershed and clustered to identify similar classes of SWE. The entire basin was then partitioned into zones for each survey date. Statistical analysis showed that partitioning the watershed on the basis of topographic and radiation variables does produce superior results over a simple random sample.
Landslides are one of the major geo-hazards which have constantly affected Italy especially over the last few years. In fact 82% of the Italian territory is affected by this phenomenon which destroys the environment and often causes deaths: therefore it is necessary to monitor these effects in order to detect and prevent these risks. Nowadays, most of this type of monitoring is carried out by using traditional topographic instruments (e.g. total stations) or satellite techniques such as global navigation satellite system (GNSS) receivers. The level of accuracy obtainable with these instruments is sub-centimetrical in post-processing and centimetrical in real-time; however, the costs are very high (many thousands of euros). The rapid diffusion of GNSS networks has led to an increase of using mass-market receivers for real-time positioning. In this paper, the performances of GNSS mass-market receiver are reported with the aim of verifying if this type of sensor can be used for real-time landslide monitoring: for this purpose a special slide was used for simulating a landslide, since it enabled us to give manual displacements thanks to a micrometre screw. These experiments were also carried out by considering a specific statistical test (a modified Chow test) which enabled us to understand if there were any displacements from a statistical point of view in real time. The tests, the algorithm and results are reported in this paper.
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Service-oriented architecture in a distributed computing environment, with loosely coupled geo-services is a new approach for using GIS services in environmental modeling. The messages exchanged must follow a set of standard protocols which support syntactic interoperability, but do not address application semantics.This article proposes a layer-based ontology with additional layers for describing geo-services, especially the measurement units used. The paper gives an ontology of measurements for describing the input and output of field-based geo-services and a core ontology of geo-services containing the domain concepts. An upper ontology adds new general concepts to an existing ontology in order to achieve an agreement between geo-service developers and environmental modelers. The layer-based structure is the building block for discovering geo-services that support semantic interoperability in GIS and environmental modeling.