The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

Time series data acquired from sensors deployed in smart homes present valuable information for intelligent systems to learn activity patterns of occupants. With the increasing need to enable people to age in place independently, the availability of such data is key to the development of home monitoring solutions. In this article we describe an unlabelled dataset of measurements collected from multiple environmental sensors placed in a smart home to capture human activities of daily living. Various sensors were used including passive infrared, force sensing resistors, reed switches, mini photocell light sensors, temperature and humidity, and smart plugs. The sensors record data from the user’s interactions with the environment, such as indoor movements, pressure applied on the bed, or current consumption when using electrical appliances. Millions of raw sensor data samples were collected continuously at a frequency of 1 Hz over a period of six months between 26 February 2020 and 26 August 2020. The dataset can be useful in the analysis of different methods, including data-driven algorithms for activity or habit recognition. In particular, the research community might be interested in investigating the performance of algorithms when applied on unlabelled datasets and not necessarily on annotated datasets. Furthermore, by applying artificial intelligence (AI) algorithms on such data collected over long periods, it is possible to extract patterns that reveal the user’s habits as well as detect changes in the habits. This can benefit in detecting deviations in order to provide timely interventions for patients, e.g., people with dementia.

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

The complexities arising from disparate data sources, conflicting contracts, residency requirements, and the demand for multiple AI models in trade finance supply chains have hindered small and medium-sized enterprises (SMEs) with limited resources from harnessing the benefits of artificial intelligence (AI) capabilities, which could otherwise enhance their business efficiency and predictability. This paper introduces a decentralized AI orchestration framework that prioritizes transparency and explainability, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility. By utilizing an orchestration technique involving symbolic reasoners, language models, and data-driven predictive tools, the framework empowers funders to make more informed decisions regarding cash flow prediction, finance rate optimization, and ecosystem risk assessment, ultimately facilitating improved access to pre-shipment trade finance for SMEs and enhancing overall supply chain operations.

Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a