Hy Dao

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Abstract Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities. EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form. Systematic and regular provision of Analysis Ready Data (ARD) will significantly reduce the burden on EO data users. Nevertheless, ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task. This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains. The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.

Operationalizing a contested concept: indicators of territorial cohesion

ABSTRACT European territorial policies increasingly refer to broad goals such as sustainability, well-being and cohesion. For their operationalization as political goals, there is a need to create indicators. But how can an indicator be meaningful in relation to a complex concept that is not well defined? If the creation of an indicator is primarily aimed at quantifying a concept and making it more operational, in the case of a contested concept (such as territorial cohesion), the process of building indicators also helps stabilize it, allowing a potential decontestation. A participatory constructivist approach – as used by the ESPON project Indicators for Territorial Cohesion (INTERCO) – is considered to be the most suitable for building indicators of contested concepts. While ensuring computability of the indicators, it allows more flexibility and reconciles the different meanings of the concept in order to stabilize it. Ultimately, the validity of indicators of contested concepts lies in pragmatic criteria: usability, usefulness and use.

An Ontology-based Algorithm for Managing the Evolution of Multi-Level Territorial Partitions

Through times, regions all over the world are very often subject to change (their names, their belonging, their composition, and their geometries). In this paper, we present a Semantic Matching Algorithm for automatically detecting, describing and publishing in the Linked Open Data Web, rich descriptions of changes occurring in multi-level territorial partitions (e.g., partitions made of major regions, regions and districts levels). We adopt a Linked Data (LD) approach for the semantic descriptions of the changes they undergo, relying on two existing generic ontologies, TSN-Ontology and TSN-Change Ontology. The created RDF graphs draw the lineage of each region over time (horizontal reading of the graphs), as well as the propagation of a change event through the partition levels (vertical reading).

Participatory Approach For Flood Risk Assessment: The Case Of Yeumbeul Nord (YN), Dakar, Senegal

Flooding has emerged lately as a major threat for poor people in the suburban area of Dakar (Senegal), a densely populated area. In this region, flood events are mainly controlled by rainfall intensity and groundwater level fluctuations. To assess flood risk at a neighbourhood level, accurate data on flood extent, exposure and vulnerability is required. The objective of the present study is to obtain these data combining remote sensing data and local knowledge. Field work data were collected through interviews with inhabitants. About 500 respondents were surveyed with the support of a local association and very detailed mapping was carried out to clearly identify elements at risk. Information collected at the household level concerns: 1) socio-economic data, 2) information on the property, 3) flooded houses and 4) strategies of risk reduction. This research demonstrates that local knowledge is an important tool to obtain accurate data useful for understanding flood hazard and vulnerability patterns. It provides quantitative data at the household level that can be used to complement conventional GIS and remote sensing data.