Abstract
This paper describes an integrated application that automates the procedure for sea outfall discharges data acquisition with an Autonomous Underwater Vehicle (AUV). Since most applications for this type of technology are research related, the used software tends to be more technical, oriented for engineers. This fact, allied with the bad sea conditions usually encountered at the portuguese coast, cause the mission execution to be extremely difficult at times. Before starting operating the AUV, a wide range of operations must be completed: we need to get data to estimate plume position, calculate mission path, transfer the AUV and acoustic buoys to the water, test communications and configure a variety of systems. So clearly there is a need to develop an application that fully automates a monitoring mission, allowing the operator with little to no experience to conclude it efficiently. Ultimately, by automating the procedure, there is the possibility of expanding the use of AUV's across several fields of study since no prior knowledge about the its systems is required. In summary this guides the user through a series of tasks and provides visual and audio information.

Abstract
The wastewater plumes show to be very difficult to observed in detail. The several studies already conducted exhibit very complex and patchy structures both in vertical and horizontal sections. It is not clear if this plume patchiness is due to physical processes or measurement limitations. Rapid tow-yo sampling is expected to reduce the time variability during and between transects. The AUVs may be a useful instrument to map and detect wastewater plumes. This paper presents several prediction studies using time series files of actual in-situ measurements integrated in a near field model. The model predictions of the plume characteristics at the end of near field support the definition of the best sampling strategy for an AUV monitoring mission in a Portuguese west coast outfall.

Abstract
This paper describes the 5dpo team. The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Abstract
This paper describes the 5dpo-2000 team, The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Abstract
Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.

Abstract
BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps. Dataset: https://doi.org/10.5281/zenodo.7948116 Dataset License: Creative Commons Attribution 4.0 International License (CC BY 4.0).