Cultural Heritage (CH) (In the context of this paper, we consider cultural heritage built tangible cultural heritage, such as buildings or monuments.) is an important source of identity for humankind and needs to be conserved for future generations. Climate change (CC) will morph the environmental landscape, thus leading to climate stress imposed on CH. Experts from different domains, including, but not limited to, material scientists, conservators and managers of cultural heritage collaborate to find out how CC affects CH and how potentially harmful impacts can be mitigated. To find and understand correlations and effects of different factors, researchers collect and analyse vast amounts of data. Still, experts often cannot exchange or make efficient use of data since it often is unstructured, incompatible, or its plain existence is simply unknown. This article introduces means to achieve consent about available knowledge, to exploit synergy effects through the combination of available information and to provide a flexible multisource information platform in collaborative cultural heritage management projects. In the context of the European project HERACLES (HERACLES—HEritage Resilience Against CLimate Events on Site. Further information: http://www.heracles-project.eu/), an application-ontology was developed. The ontology facilitates reuse and integration of data through structuring and representing its semantics. The involvement in the HERACLES project guaranteed end-user driven development, practical results and encompassment of all domains represented in the project.

Due to the increasing threat posed by crime, industrial espionage and even terrorism, video surveillance systems have become more important and powerful during the last years. While most commercially available surveillance systems have to be managed by human operators, who constantly monitor all video streams, several experimental systems from different research groups already include robust video processing approaches for (semi-) automated surveillance. Still, most research activities focus on a sensor-oriented approach to video analytics of large and distributed camera networks, aiming to extract, analyze and store all extractable information from the video streams. In real-life applications, however, only a limited set of specific threats needs to be covered. Accordingly, only a small subset of potentially extractable information has to be monitored. Besides the huge amount of raw video data, modern surveillance systems are also extended with other sensors that deliver even more data. As a consequence, a new paradigm is introduced, called task-oriented information and data processing for surveillance systems. In the proposed system NEST (Network Enabled Surveillance and Tracking) following the task-oriented approach, every resource allocation, data acquisition, and analysis process is assigned to a specific surveillance task. In order to meet the requirements of task-oriented surveillance, the proposed architecture combines a Service-Oriented Architecture with an Event-Driven Architecture (Event-driven SOA).

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

Extraction and analysis of environmental information is very important, since it strongly affects everyday life. Nowadays there are already many free services providing environmental information in several formats including multimedia (e.g. map images). Although such presentation formats might be very informative for humans, they complicate the automatic extraction and processing of the underlying data. A characteristic example is the air quality and pollen forecasts, which are usually encoded in image maps, while the initial (numerical) pollutant concentrations remain unavailable. This work proposes a framework for the semi-automatic extraction of such information based on a template configuration tool, on Optical Character Recognition (OCR) techniques and on methodologies for data reconstruction from images. The system is tested with a different air quality and pollen forecast heatmaps demonstrating promising results.

Environmental data analysis and information provision are considered of great importance for people, since environmental conditions are strongly related to health issues and directly affect a variety of everyday activities. Nowadays, there are several free web-based services that provide environmental information in several formats with map images being the most commonly used to present air quality and pollen forecasts. This format, despite being intuitive for humans, complicates the extraction and processing of the underlying data. Typical examples of this case are the chemical weather forecasts, which are usually encoded heatmaps (i.e. graphical representation of matrix data with colors), while the forecasted numerical pollutant concentrations are commonly unavailable. This work presents a model for the semi-automatic extraction of such information based on a template configuration tool, on methodologies for data reconstruction from images, as well as on text processing and Optical Character Recognition (OCR). The aforementioned modules are integrated in a standalone framework, which is extensively evaluated by comparing data extracted from a variety of chemical weather heat maps against the real numerical values produced by chemical weather forecasting models. The results demonstrate a satisfactory performance in terms of data recovery and positional accuracy