Jean-Paul Kasprzyk

Bio: Jean-Paul Kasprzyk is an academic researcher from University of Liège. The author has contributed to research in topics: Raster graphics & NoSQL. The author has an hindex of 3, co-authored 14 publications receiving 53 citations.

Un regard sur l’évolution de la géomatique : analyse de 4 décennies de recherche au sein du Département de Géographie de l’Université de Liège

Abstract: La geomatique est un domaine en pleine evolution directement impacte par les progres des technologies de l’information et de la communication. En 40 ans, les changements de pratiques ont ete radicaux (des appareils optiques et du support papier aux capteurs numeriques sophistiques et a la dematerialisation de l’information). Cet article pose un regard sur l’evolution de la discipline a travers l’analyse de 4 decennies de recherche au sein de l’Unite de Geomatique de l’Universite de Liege. Cette analyse se base d’une part sur une etude historique des postes permanents dans le domaine de la geomatique au sein du Departement de Geographie. Ensuite, les trajectoires de recherche de l’Unite sont presentees a partir de la situation etablie en 2003. Une evaluation de ces recherches est enfin effectuee a travers plusieurs criteres que sont les publications, les projets, l’impact sur l’enseignement et l’impact sur le monde professionnel. Meme si cette analyse presente un biais methodologique vu qu’elle ne porte que sur une seule Unite d’enseignement et de recherche, des constats generaux peuvent tout de meme etre dresses. Il ressort que la geomatique evolue a un tel rythme qu’elle deborde tres largement des sciences geographiques tout en y etant solidement ancree. Son enseignement et sa recherche necessitent des ressources qui sont la plupart du temps difficiles a obtenir, ce qui a pour consequence de limiter son role a un support technologique pour d’autres disciplines. En ce qui concerne specifiquement l’Unite de Geomatique, cette analyse permet de dresser un etat des lieux necessaire a un moment cle de sa vie.

A Built Heritage Information System Based on Point Cloud Data: HIS-PC

Abstract: The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials are managed from a central database and visualised through a 3D representation. In this research, we offer the development of a built heritage information system prototype based on a high-resolution 3D point cloud data set. The particularity of the approach is to consider a user-centred development methodology while avoiding meshing/down-sampling operations. The proposed system is initiated by a close collaboration between multi-modal users (managers, visitors, curators) and a development team (designers, developers, architects). The developed heritage information system permits the management of spatial and temporal information, including a wide range of semantics using relational along with NoSQL databases. The semantics used to describe the artifacts are subject to conceptual modelling. Finally, the system proposes a bi-directional communication with a 3D interface able to stream massive point clouds, which is a big step forward to provide a comprehensive site representation for stakeholders while minimising modelling costs.

Towards an ontology for the structuring of remote sensing operations shared by different processing chains

Abstract: . This paper proposes an ontology to structure and describe processing chains in the remote sensing field. These chains are made up of elementary elements (operations) organized in collections. The collection notion, including information about order and repeatability of the elements, is widely defined by using the relations between their constituting items and relations to the whole data store. Applications of the ontology are illustrated with web services provided by a platform for users and providers of processing chains. A graphical interface facilitates data integration in a RDF triple store. Thanks to the management of metadata (ISO19115-3), relevant information can be requested by intelligent search engines. Graph analysis, errors management and consistency rules are computed in order to gather coherent information from the different sources. Results of these analyses are then used by machine learning algorithms for new knowledge discovery.

Coupling an Unstructured NoSQL Database with a Geographic Information System

Abstract: The management of unstructured NoSQL (Not only Structured Query Language) databases has undergone a great development in the last years mainly thanks to Big Data. Nevertheless, the specificity of spatial information is not purposely taken into account. To overcome this difficulty, we propose to couple a NoSQL database with a spatial Relational Data Base Management System (RDBMS). Exchanges of information between these two systems are illustrated with relevant examples involving spatial queries. The spatial data stored in MongoDB consists of field surveys (points, photos, etc.) and scanned plans, while reference data (cadastre) is recorded in PostGIS. The extensions required to allow this coupling are written in Python. KeywordDocument-Oriented Database; MongoDB; Spatial RDBMS; PostGIS; Spatial Queries, Python.

Maps for the rest of us: affordable and fun

Abstract: A GIS (Geographic Information System) is a computer system designed to collect, store, retrieve, manipulate, and display spatial data. This technology combines aspects of hypertext/hypermedia and database management software in a unique useful form. An overview of GIS technology, its history, the software market, and general issues is proposed.

Ontologies to interpret remote sensing images: why do we need them?

Abstract: The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.

Combining deep learning and ontology reasoning for remote sensing image semantic segmentation

Abstract: Because of its wide potential applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Until now, deep semantic segmentation network (DSSN) has achieved a certain degree of success on semantic segmentation of RS imagery and can obviously outperform the traditional methods based on hand-crafted features. As a classic data-driven technique, DSSN can be trained by an end-to-end mechanism and is competent for employing low-level and mid-level cues (i.e., the discriminative image structure) to understand RS images. However, its interpretability and reliability are poor due to the nature weakness of the data-driven deep learning methods. By contrast, human beings have an excellent inference capacity and can reliably interpret RS imagery with the basic RS domain knowledge. Ontological reasoning is an ideal way to imitate and employ the domain knowledge of human beings. However, it is still rarely explored and adopted in the RS domain. As a solution of the aforementioned critical limitation of DSSN, this study proposes a collaboratively boosting framework (CBF) to combine the data-driven deep learning module and knowledge-guided ontology reasoning module in an iterative manner. The deep learning module adopts the DSSN architecture and takes the integration of the original image and inferred channels as the input of the DSSN. In addition, the ontology reasoning module is composed of intra- and extra-taxonomy reasoning. More specifically, the intra-taxonomy reasoning directly corrects misclassifications of the deep learning module based on the domain knowledge, which is the key to improve the classification performance. The extra-taxonomy reasoning aims to generate the inferred channels beyond the current taxonomy to improve the discriminative performance of DSSN in the original RS image space. On the one hand, benefiting from the referred channels from the ontology reasoning module, the deep learning module using the integration of the original image and referred channels can achieve better classification performance than only using the original image. On the other hand, better classification results from the deep learning module further improve the performance of the ontology reasoning module. As a whole, the deep learning and ontology reasoning modules are mutually boosted in the iterations. Extensive experiments on two publicly open RS datasets such as UCM and ISPRS Potsdam show that our proposed CBF can outperform the competitive baselines with a large margin.

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Abstract: The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.