Digital twins (DTs) are highly valuable tools for urban planning as they provide a virtual replica of the physical city, integrating real-time data and simulations to enhance the decision-making and management processes. The use of DTs expands the possibilities for data integration and visualization in urban contexts. This includes real-time data measurements from multiple sources, such as sensors and IoT devices, facilitating comprehensive insights. DT’s virtual representation helps authorities and planners visualize urban dynamics and improve their understanding of urban ecosystems, energy efficiency, traffic management, emergency response, and more. DT supports the simulation and modeling of different scenarios in an urban built environment, enabling the predictive analysis of transformation decisions and the anticipation of future trends and challenges. This paper highlights the assumptions and ongoing progress in the development of a DT for the city of Turin (Italy), focusing on a range of applications, such as the extraction of built and natural environment features, land use data, road network and pavement quality, and signage, along with continuous model updates over time.

The recent innovations in remote sensing technologies have given rise to the efficient mapping and monitoring of forests. The developments in the sensor implementation have mainly focused on optimizing the payload of the UAV system and allowed the users to acquire the data simultaneously with a range of active and passive sensors like high-resolution RGB cameras and multispectral cameras LiDAR (Laser Imaging Detection and Ranging). The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two vegetation seasons, namely leaf-off and leaf-on, were used to combine the Digital Elevation Models from different data acquisition platforms. This internship research work aims to create and experiment with new methods, techniques, and technologies for the applications of UAV photogrammetry and iPhone LiDAR in forest napping and inventory management. CHMs are also generated in this work which helps assess the conditions of the forests in the recreational areas, and the possibility of solutions like iPhone LiDAR and UAV photogrammetry would be highly efficient and economical. The leaf-off and leaf-on datasets were processed in Agisoft Metashape Professional software to generate dense point clouds for the forest plots. The point cloud from the leaf-on dataset was rasterized to generate a DSM whereas the leaf-off point cloud generated a DSM of the forest plots after ground filtering with Cloth Simulation Filter (CSF) plugin. The iPhone LiDAR point was also rasterized to a DTM product after pre-processing steps and noise removal. The Canopy Height Models (CHMs) were generated by subtracting UAV and iPhone LiDAR based DTMs from the UAV leaf on DSM. Finally, the accuracy assessment of CHMs from UAB datasets and their integration with iPhone LiDAR has been assessed using the accurate tree heights measured during the forest field visits. The proposed methodology can be used for forest mapping purposes where a moderate accuracy is requested.

3D City models are essential for urban planning, accurately visualizing, analyzing, and simulating urban environments. They find applications in various fields like AEC (Architecture and Construction), urban and transportation planning, development and conservation processes, energy systems monitoring, and many more. Extensive datasets for the 3D City Models need to be organized following international standards to make them reusable and sharable for different stakeholders. Designing a Geodatabase (GeoDB) for a 3D city model is crucial for data management as it is helpful in the streamlined visualization and analysis of the city's features and relations among its objects. Over the years, 3D city models have evolved into Urban Digital Twins, offering dynamic real-time simulations of cities and even better management and analysis of urban processes. This transition from 3D city models to UDTs enhances decision-making by providing detailed insights into the dynamics of urban systems, enabling better urban management and planning. This research concentrates on building a GeoDB to support the UDT. The paper discusses the data acquisition, processing, and integration methodologies.Additionally, it highlights the significance of utilizing advanced remote sensing technologies such as aerial LiDAR and aerial photogrammetry to enhance the digital twin’s quality and richness in detail. The semantics of the built environment datasets are clarified and strictly defined for the most important UDT features, such as buildings, roads, trees, and other features. This will ensure everyone can interpret the data similarly, leading to better analysis and decision-making.

Several advancements are going with Unmanned Aerial Systems (UAS) with the addition of multiple sensors and simultaneous data acquisition to obtain detailed geo-data for various applications. However, simultaneous data acquisition with multiple sensors, namely camera, and LiDAR, will also result in possible discrepancies associated with them, and they need to be solved to use a reliable and accurate final product. Several errors can be associated with both camera and LiDAR datasets due to the different characteristics of the sensors and terrain conditions. This research paper aimed to minimize the errors between LiDAR and the image datasets simultaneously acquired with an Unmanned Aerial System (UAS) by implementing a hybrid adjustment approach with a criterion for the roughness and threshold angle between surface normals. The initial trajectory of the UAS, raw LiDAR measurements, and image observations were the inputs used for the hybrid adjustment. The hybrid adjustment workflow minimizes the discrepancies with a least-squares-based simultaneous adjustment for both LiDAR and image datasets. For the hybrid adjustment process, three types of correspondences were established, namely: between image pairs, overlapping LiDAR strips, and between Image tie points and LiDAR strips. For quality control, mean Cloud-to-Cloud distances (C2C) were compared between both LiDAR and camera point clouds before and after hybrid adjustment. The surface-level analysis of the results was also carried out to analyze the errors before and after hybrid adjustment at a surface level for different types of surfaces. The results showed that the alignment between the point clouds has significantly improved from the range of meters to a centimeter-level after implementing the hybrid adjustment process. The proposed hybrid adjustment workflow can be used in mapping applications where a centimeter-level accuracy is requested.

Unrecognised Historical places, which are locations with history but lacking official recognition, particularly prevalent in Southeast Asia, often face a dearth of accessible historical information. To tackle this challenge, there is an urgent need to commence the collection and documentation of historical data concerning Unrecognised Historical places. This initiative aims to prevent the loss of history, given that such sites are frequently subjected to rapid development and commercialization. To date, City Geography Markup Language (CityGML), an Open Geospatial Consortium (OGC) standard 3D data format, has been developed for presenting city models along with semantic data and is often used in the heritage domain for preservation. It aids in digital infrastructure development and the conservation of urban elements. Furthermore, it provides a mechanism to extend the standard data model to meet the requirements of various purposes. While several CityGML extensions tailored to the documentation of Cultural Heritage already exist, there is currently no extension specifically designed to address the requirements of Unrecognised Historical places. The aim of this paper is to identify a suitable approach for developing a new standardised data model that integrates geometric and semantic information, which is demonstrated for the modelling of Telukbetung, an old city in Bandar Lampung through time.