Development of inventory datasets through remote sensing and direct observation data for earthquake loss estimation

This report summarizes the lessons learnt in extracting exposure information for the three study sites, Thessaloniki, Vienna and Messina that were addressed in SYNER-G. Fine scale information on exposed elements that for SYNER-G include buildings, civil engineering works and population, is one of the variables used to quantify risk. Collecting data and creating exposure inventories is a very time-demanding job and all possible data-gathering techniques should be used to address the data shortcoming problem. This report focuses on combining direct observation and remote sensing data for the development of exposure models for seismic risk assessment. In this report a summary of the methods for collecting, processing and archiving inventory datasets is provided in Chapter 2. Chapter 3 deals with the integration of different data sources for optimum inventory datasets, whilst Chapters 4, 5 and 6 provide some case studies where combinations between direct observation and remote sensing have been used. The cities of Vienna (Austria), Thessaloniki (Greece) and Messina (Italy) have been chosen to test the proposed approaches.JRC.G.5-European laboratory for structural assessmen

OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS

open9siAccurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy).openToschi, I.; Ramos, M. M.; Nocerino, E.; Menna, F.; Remondino, F.; Moe, K.; Poli, D.; Legat, K.; Fassi, F.Toschi, I.; Ramos, M. M.; Nocerino, E.; Menna, F.; Remondino, F.; Moe, K.; Poli, D.; Legat, K.; Fassi, Francesc

OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS

Accurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy)

OBLIQUE MULTI-CAMERA SYSTEMS - ORIENTATION AND DENSE MATCHING ISSUES

International audience3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy <rupnik, franex, remondino>@fbk.eu, http://3dom.fbk.eu Commission III-WG4 ABS TRACT: The use of oblique imagery has become a standard for many civil and mapping applications, thanks to the development of airborne digital multi-camera systems, as proposed by many companies (Blomoblique, IGI, Leica, M idas, Pictometry, Vexcel/M icrosoft, VisionM ap, etc.). The indisputable virtue of oblique photography lies in its simplicity of interpretation and understanding for inexperienced users allowing their use of oblique images in very different applications, such as building detection and reconstruction, building structural damage classification, road land updating and administration services, etc. The paper reports an overview of the actual oblique commercial systems and presents a workflow for the automated orientation and dense matching of large image blocks. Perspectives, potentialities, pitfalls and suggestions for achieving satisfactory results are given. Tests performed on two datasets acquired with two multi-camera systems over urban areas are also reported. Figure 1: Large urban area pictured with an oblique multi-camera system. Once advanced image triangulation methods have retrieved interior and exterior parameters of the cameras, dense point clouds can be deriv ed for 3D city modelling, feature extraction and mapping purposes

Automatic manhole extraction from MMS data to update basemaps

Basemaps are the main resource used in urban planning, building and infrastructure asset management. Therefore, they must be accurate and up to date to better serve citizens, contractors, property owners and town planning departments. Traditionally, they have been updated by aerial photogrammetry, but this is not always possible and alternatives need to be sought. In such cases, a useful option for large scales is the mobile mapping system (MMS). However, automatic extraction from MMS point clouds is limited by the complexity of the urban environment. Therefore, the influence of the urban pattern is analysed in three zones with varied urban characteristics: areas with high buildings, open areas, and areas with a low level of urbanization. In these areas, the capture and automatic extraction of 3D urban elements is performed using commercial software, which is useful for some elements but not for manholes. The objective of this study is to establish a methodology for extracting manholes automatically and completing hidden buildings' corners, in order to update urban basemaps. Shape and intensity are the main detection parameters for manholes, whereas additional information from satellite image Quickbird is used to complete the buildings. The worst rate of detection for all the extracted urban elements was found in areas of high buildings. Finally, the article analyses the computing cost for manhole extraction, and the economic cost and time consume of the entire process, including the proposed methodolgy using an MMS point cloud and the traditional survey in this case.Peer ReviewedPostprint (updated version

Photogrammetric suite to manage the survey workflow in challenging environments and conditions

The present work is intended in providing new and innovative instruments to support the photogrammetric survey workflow during all its phases. A suite of tools has been conceived in order to manage the planning, the acquisition, the post-processing and the restitution steps, with particular attention to the rigorousness of the approach and to the final precision. The main focus of the research has been the implementation of the tool MAGO, standing for Adaptive Mesh for Orthophoto Generation. Its novelty consists in the possibility to automatically reconstruct \u201cunrolled\u201d orthophotos of adjacent fa\ue7ades of a building using the point cloud, instead of the mesh, as input source for the orthophoto reconstruction. The second tool has been conceived as a photogrammetric procedure based on Bundle Block Adjustment. The same issue is analysed from two mirrored perspectives: on the one hand, the use of moving cameras in a static scenario in order to manage real-time indoor navigation; on the other hand, the use of static cameras in a moving scenario in order to achieve the simultaneously reconstruction of the 3D model of the changing object. A third tool named U.Ph.O., standing for Unmanned Photogrammetric Office, has been integrated with a new module. The general aim is on the one hand to plan the photogrammetric survey considering the expected precision, computed on the basis of a network simulation, and on the other hand to check if the achieved survey has been collected compatibly with the planned conditions. The provided integration concerns the treatment of surfaces with a generic orientation further than the ones with a planimetric development. After a brief introduction, a general description about the photogrammetric principles is given in the first chapter of the dissertation; a chapter follows about the parallelism between Photogrammetry and Computer Vision and the contribution of this last in the development of the described tools. The third chapter specifically regards, indeed, the implemented software and tools, while the fourth contains the training test and the validation. Finally, conclusions and future perspectives are reported

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor

Concepts and tools to improve the thermal energy performance of buildings and urban districts - diagnosis, assessment, improvement strategies and cost-benefit analyses

Retrofitting existing buildings to optimize their thermal energy performance is a key factor in achieving climate neutrality by 2045 in Germany. Analyzing buildings in their current condition is the first step toward preparing effective and efficient energy retrofit measures. A high-quality building analysis helps to evaluate whether a building or its components are suitable for retrofitting or replacement. Subsequently, appropriate combinations of retrofit measures that create financial and environmental synergies can be determined. This dissertation is a cumulative work based on nine papers on the thermal analysis of existing buildings. The focus of this work and related papers is on thermography with drones for building audits, intelligent processing of thermographic images to detect and assess thermal weaknesses, and building modeling approaches to evaluate thermal retrofit options. While individual buildings are usually the focus of retrofit planning, this dissertation also examines the role of buildings in the urban context, particularly on a district level. Multiple adjacent buildings offer numerous possibilities for further improving retrofits, such as the economies of scale for planning services and material procurement, neighborhood dynamics, and exchange of experiences between familiar building owners. This work reveals the opportunities and obstacles for panorama drone thermography for building audits. It shows that drones can contribute to a quick and structured data collection, particularly for large building stocks, and thus complement current approaches for district-scale analysis. However, the significant distance between the drone camera and building, which is necessary for automated flight routes, and varying recording angles limit the quantitative interpretability of thermographic images. Therefore, innovative approaches were developed to process image datasets generated using drones. A newly designed AI-based approach can automate the detection of thermal bridges on rooftops. Using generalizations about certain building classes as demonstrated by buildings from the 1950s and 1960s, a novel interpretation method for drone images is suggested. It enables decision-making regarding the need to retrofit thermal bridges of recorded buildings. A novel optimization model for German single-family houses was developed and applied in a case study to investigate the financial and ecological benefits of different thermal retrofit measures. The results showed that the retrofitting of building façades can significantly save energy. However, they also revealed that replacing the heating systems turns out to be more cost-effective for carbon dioxide savings. Small datasets, limited availability of technical equipment, and the need for simplified assumptions for building characteristics without any information were the main challenges of the approaches in this dissertation