The colorization of grayscale images can, nowadays, take advantage of recent progress and the automation of deep-learning techniques. From the media industry to medical or geospatial applications, image colorization is an attractive and investigated image processing practice, and it is also helpful for revitalizing historical photographs. After exploring some of the existing fully automatic learning methods, the article presents a new neural network architecture, Hyper-U-NET, which combines a U-NET-like architecture and HyperConnections to handle the colorization of historical black and white aerial images. The training dataset (about 10,000 colored aerial image patches) and the realized neural network are available on our GitHub page to boost further research investigations in this field.

Urban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution gap between high-resolution optical data and low-resolution satellite thermal imagery. This study introduces a novel deep learning approach—named Dilated Spatio-Temporal U-Net (DST-UNet)—to bridge this gap. DST-UNET is a modified U-Net architecture which incorporates dilated convolutions to address the multiscale nature of urban thermal patterns. The model is trained to generate high-resolution, airborne-like thermal maps from available, low-resolution satellite imagery and ancillary data. Our results demonstrate that the DST-UNet can effectively generalise across different urban environments, enabling municipalities to generate detailed thermal maps with a frequency far exceeding that of traditional airborne campaigns. This framework leverages open-source data from missions like Landsat to provide a cost-effective and scalable solution for continuous, high-resolution urban thermal monitoring, empowering more effective climate resilience and public health initiatives.

We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child’s current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child’s performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments.

Recent advancements in aerial imaging, including high-resolution sensors and integrated GNSS/IMU systems, have significantly enhanced photogrammetric methods for geospatial data acquisition. While most aerial data is captured during daylight, night-time imaging is increasingly being used in applications such as urban analysis and disaster assessment. However, automatic co-registration of day and night imagery remains challenging due to substantial radiometric differences. This study investigates the use of deep learning-based feature matching techniques for the alignment of multi-temporal, day-night aerial datasets. Experimental results show that feature extraction is highly sensitive to scale, with only a limited subset of deep learning (DL) methods—particularly ALIKED with LightGlue and SuperPoint with SuperGlue—proving robust under low-illumination conditions. Additionally, a U-Net-like model was trained to pre-process night-time images by approximating their radiometric characteristics to those of daytime images, enabling consistent feature matching across all tested methods. Among them, ALIKED with LightGlue offered the best balance between match quantity and computational efficiency. Object-space evaluations confirmed that the proposed pre-processing step significantly improves co-registration accuracy. The methodology offers a promising foundation for future multi-sensor and multi-modal image alignment tasks, including RGB-thermal and 2D-3D matching.

Traditionally, drawing products created from 3D surveying activities have been the universal medium of communication used by architects. This has resulted in a vast repository of graphic documentation that serves as a testament of the architectural heritage. The embedded information found in elevations, plans and sections holds considerable value, and it can be seamlessly integrated into the intricate graphics produced during large-scale data acquisition processes. The core objective of this research is to investigate how the information coming from the large amount of existing architectural technical drawings can support 3D heritage classification processes and avoid time-consuming annotation of materials and construction techniques of historical building facades. Starting from available sets of drawings, AI-based methodologies are applied for the annotation of orthoimages and point clouds in order to obtain a predictive model that can recognize classes of materials and construction techniques in a large amount of data. The predicted classes also allow the automatic creation of vector drawing representing the facades of new buildings, providing a novel tool to facilitate the processes of analysis and conservation of architectural heritage.