ABSTRACT:The process of capturing and modelling buildings has gained increased focus in recent years with the rise of Building Information Modelling (BIM). At the heart of BIM is a process change for the construction and facilities management industries whereby a BIM aids more collaborative working through better information exchange, and as a part of the process Geomatic/Land Surveyors are not immune from the changes. Terrestrial laser scanning has been proscribed as the preferred method for rapidly capturing buildings for BIM geometry. This is a process change from a traditional measured building survey just with a total station and is aided by the increasing acceptance of point cloud data being integrated with parametric building models in BIM tools such as Autodesk Revit or Bentley Architecture. Pilot projects carried out previously by the authors to investigate the geometry capture and modelling of BIM confirmed the view of others that the process of data capture with static laser scan setups is slow and very involved requiring at least two people for efficiency. Indoor Mobile Mapping Systems (IMMS) present a possible solution to these issues especially in time saved. Therefore this paper investigates their application as a capture device for BIM geometry creation over traditional static methods through a fit-for-purpose test.

Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites— ’CubeSats’ are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products. The ESA’s recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach.

The documentation of heritage buildings is the preliminary action to deal with any problem related to the built heritage. The procedure of documentation requires a very diverse range of data (quantitative and qualitative) to be obtained and investigated in order to produce an accurate digital representation of the building. This type of work of data capture and interpretation is often conducted in isolation by different stakeholders and for a range of purposes, leading to a lack of communication between different data types, repeated effort and incomplete documentation. Heritage Building Information Modelling (H-BIM) is set to play a key role in the digital documentation of heritage buildings, as it can combine quantitative and qualitative data and facilitate the integration of different stakeholders and specialised data into the digital management of the different phases of dealing with heritage buildings. This paper aims to review the multitude of data types that could be included in the documentation and investigation process of the built heritage, in order to assess the breadth and depth by which heritage buildings can be documented. Four main categories that span the whole documentation data areas are being suggested which vary from outer geometry surveys, to subsurface materials and structural integrity investigations, to data concerning the building performance, as well as the historic records concerning the building’s morphology over time, which can help to create a more in-depth knowledge about the heritage building’s status and performance and can create a solid base for any required restoration and retrofitting processes (Khalil and Stravoravdis 2019a).

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<p><strong>Abstract.</strong> The occurrence of urban flooding following strong rainfall events may increase as a result of climate change. Urban expansion, aging infrastructure and an increasing number of impervious surfaces are further exacerbating flooding. To increase resilience and support flood mitigation, bespoke accurate flood modelling and reliable prediction is required. However, flooding in urban areas is most challenging. State-of-the-art flood inundation modelling is still often based on relatively low-resolution 2.5&amp;thinsp;D bare earth models with 2&amp;ndash;5&amp;thinsp;m GSD. Current systems suffer from a lack of precise input data and numerical instabilities and lack of other important data, such as drainage networks. Especially, the quality and resolution of the topographic input data represents a major source of uncertainty in urban flood modelling. A benchmark study is needed that defines the accuracy requirements for highly detailed urban flood modelling and to improve our understanding of important threshold processes and limitations of current methods and 3D mapping data alike.</p><p>This paper presents the first steps in establishing a new, innovative multiscale data set suitable to benchmark urban flood modelling. The final data set will consist of high-resolution 3D mapping data acquired from different airborne platforms, focusing on the use of drones (optical and LiDAR). The case study includes residential as well as rural areas in Dudelange/Luxembourg, which have been prone to localized flash flooding following strong rainfall events in recent years. The project also represents a cross disciplinary collaboration between the geospatial and flood modelling community. In this paper, we introduce the first steps to build up a new benchmark data set together with some initial flood modelling results. More detailed investigations will follow in the next phases of this project.</p>

Very high-resolution (VHR) optical Earth observation (EO) satellites as well as lowaltitude and easy-to-use unmanned aerial systems (UAS/drones) provide ever-improving data sources for the generation of detailed 3-dimensional (3D) data using digital photogrammetric methods with dense image matching. Today both data sources represent cost-effective alternatives to dedicated airborne sensors, especially for remote regions. The latest generation of EO satellites can collect VHR imagery up to 0.30 m ground sample distance (GSD) of even the most remote location from different viewing angles many times per year. Consequently, well-chosen scenes from growing image archives enable the generation of high-resolution digital elevation models (DEMs). Furthermore, low-cost and easy to use drones can be quickly deployed in remote regions to capture blocks of images of local areas. Dense point clouds derived from these methods provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. Here we investigate the use of archived VHR satellite imagery with approx. 0.5 m GSD as well as low-altitude drone-based imagery with average GSD of better than 0.03 m to generate high-quality DEMs using photogrammetric tools over Tristan da Cunha, a remote island in the South Atlantic Ocean which lies beyond the reach of current commercial manned airborne mapping platforms. This study explores the potentials and limitations to combine this heterogeneous data sources to generate improved DEMs in terms of accuracy and resolution. A cross-validation between low-altitude airborne and spaceborne data sets describes the fit between both optical data sets. No co-registration error, scale difference or distortions were detected, and a quantitative cloud-to-cloud comparison showed an average distance of 0.26 m between both point clouds. Both point clouds were merged applying a conventional georeferenced approach. The merged DEM preserves the rich detail from the drone-based survey and provides an accurate 3D representation of the entire study area. It provides September 2020 | Volume 8 | Article 319 Backes and TeferleMultiscale DEM Fusion Over Tristan the most detailed model of the island to date, suitable to support practical and scientific applications. This study demonstrates that combination archived VHR satellite and low-altitude drone-based imagery provide inexpensive alternatives to generate high-quality DEMs.

Abstract. An efficient alternative to labour-intensive terrestrial and costly airborne surveys is the use of small, inexpensive Unmanned Aerial Vehicles (UAVs) or Remotely Piloted Aerial Systems (RPAS). These low-altitude remote sensing platforms, commonly known as drones, can carry lightweight optical and LiDAR sensors. Even though UAV systems still have limited endurance, they can provide a flexible and relatively inexpensive monitoring solution for a limited area of interest. This study investigated the applicability of monitoring the morphology of a frequently changing glacial stream using high-resolution topographic surface models derived from low-altitude UAV-based photogrammetry and LiDAR. An understanding of river-channel morphology and its response to anthropogenic and natural disturbances is imperative for effective watershed management and conservation. We focus on the data acquisition, processing workflow and highlight identified challenges and shortcomings. Additionally, we demonstrate how LiDAR data acquisition simulations can help decide which laser scanning approach to use and help optimise data collection to ensure full coverage with desired level of detail. Lastly, we showcase a case study of 3D surface change analysis in an alpine stream environment with UAV-based photogrammetry. The datasets used in this study were collected as part of the ISPRS Summer School of Alpine Research, which will continue to add new data layers on a biyearly basis. This growing data repository is freely available for research.

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