06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7

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Institute: search.filters.UBSInstitut.Institut für PhotogrammetrieStart date: 2023

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    Forming a hybrid intelligence system by combining Active Learning and paid crowdsourcing for semantic 3D point cloud segmentation
    (2023) Kölle, Michael; Sörgel, Uwe (Prof. Dr.-Ing.)
    While in recent years tremendous advancements have been achieved in the development of supervised Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs), still the most decisive factor for their performance is the quality of labeled training data from which the system is supposed to learn. This is why we advocate focusing more on methods to obtain such data, which we expect to be more sustainable than establishing ever new classifiers in the rapidly evolving ML field. In the geospatial domain, however, the generation process of training data for ML systems is still rather neglected in research, with typically experts ending up being occupied with such tedious labeling tasks. In our design of a system for the semantic interpretation of Airborne Laser Scanning (ALS) point clouds, we break with this convention and completely lift labeling obligations from experts. At the same time, human annotation is restricted to only those samples that actually justify manual inspection. This is accomplished by means of a hybrid intelligence system in which the machine, represented by an ML model, is actively and iteratively working together with the human component through Active Learning (AL), which acts as pointer to exactly such most decisive samples. Instead of having an expert label these samples, we propose to outsource this task to a large group of non-specialists, the crowd. But since it is rather unlikely that enough volunteers would participate in such crowdsourcing campaigns due to the tedious nature of labeling, we argue attracting workers by monetary incentives, i.e., we employ paid crowdsourcing. Relying on respective platforms, typically we have access to a vast pool of prospective workers, guaranteeing completion of jobs promptly. Thus, crowdworkers become human processing units that behave similarly to the electronic processing units of this hybrid intelligence system performing the tasks of the machine part. With respect to the latter, we do not only evaluate whether an AL-based pipeline works for the semantic segmentation of ALS point clouds, but also shed light on the question of why it works. As crucial components of our pipeline, we test and enhance different AL sampling strategies in conjunction with both a conventional feature-driven classifier as well as a data-driven CNN classification module. In this regard, we aim to select AL points in such a manner that samples are not only informative for the machine, but also feasible to be interpreted by non-experts. These theoretical formulations are verified by various experiments in which we replace the frequently assumed but highly unrealistic error-free oracle with simulated imperfect oracles we are always confronted with when working with humans. Furthermore, we find that the need for labeled data, which is already reduced through AL to a small fraction (typically ≪1 % of Passive Learning training points), can be even further minimized when we reuse information from a given source domain for the semantic enrichment of a specific target domain, i.e., we utilize AL as means for Domain Adaptation. As for the human component of our hybrid intelligence system, the special challenge we face is monetarily motivated workers with a wide variety of educational and cultural backgrounds as well as most different mindsets regarding the quality they are willing to deliver. Consequently, we are confronted with a great quality inhomogeneity in results received. Thus, when designing respective campaigns, special attention to quality control is required to be able to automatically reject submissions of low quality and to refine accepted contributions in the sense of the Wisdom of the Crowds principle. We further explore ways to support the crowd in labeling by experimenting with different data modalities (discretized point cloud vs. continuous textured 3D mesh surface), and also aim to shift the motivation from a purely extrinsic nature (i.e., payment) to a more intrinsic one, which we intend to trigger through gamification. Eventually, by casting these different concepts into the so-called CATEGORISE framework, we constitute the aspired hybrid intelligence system and employ it for the semantic enrichment of ALS point clouds of different characteristics, enabled through learning from the (paid) crowd.
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    CRBeDaSet : a benchmark dataset for high accuracy close range 3D object reconstruction
    (2023) Gabara, Grzegorz; Sawicki, Piotr
    This paper presents the CRBeDaSet - a new benchmark dataset designed for evaluating close range, image-based 3D modeling and reconstruction techniques, and the first empirical experiences of its use. The test object is a medium-sized building. Diverse textures characterize the surface of elevations. The dataset contains: the geodetic spatial control network (12 stabilized ground points determined using iterative multi-observation parametric adjustment) and the photogrammetric network (32 artificial signalized and 18 defined natural control points), measured using Leica TS30 total station and 36 terrestrial, mainly convergent photos, acquired from elevated camera standpoints with non-metric digital single-lens reflex Nikon D5100 camera (ground sample distance approx. 3 mm), the complex results of the bundle block adjustment with simultaneous camera calibration performed in the Pictran software package, and the colored point clouds (ca. 250 million points) from terrestrial laser scanning acquired using the Leica ScanStation C10 and post-processed in the Leica Cyclone™ SCAN software (ver. 2022.1.1) which were denoized, filtered, and classified using LoD3 standard (ca. 62 million points). The existing datasets and benchmarks were also described and evaluated in the paper. The proposed photogrammetric dataset was experimentally tested in the open-source application GRAPHOS and the commercial suites ContextCapture, Metashape, PhotoScan, Pix4Dmapper, and RealityCapture. As the first experience in its evaluation, the difficulties and errors that occurred in the software used during dataset digital processing were shown and discussed. The proposed CRBeDaSet benchmark dataset allows obtaining high accuracy (“mm” range) of the photogrammetric 3D object reconstruction in close range, based on a multi-image view uncalibrated imagery, dense image matching techniques, and generated dense point clouds.
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    On the analysis and patterns of persistent scatterer interferometry results for satellite-based deformation monitoring
    (2023) Schneider, Philipp J.; Sörgel, Uwe (Prof. Dr.-Ing.)
    The remote sensing method Persistent Scatterer Interferometry (PSI) has developed in the last two decades into a tool for monitoring deformations of the earths surface. Hereby it can be applied to large areas and scenes like earth quakes, landslides or sinkholes but the increasing availability of high resolution Synthetic Aperture Radar (SAR) data also enables a monitoring of small scenes like individual buildings. PSI is recognized and appreciated in the remote sensing community and its benefit has been proven in countless applications. The PSI principle is based on the evaluation of time series of coherent SAR satellite images and considers the relative phase change over time for individual pixels. As a result of this interferometric evaluation, time series are obtained, which capture line-of-sight (LOS) movement of a scatterer over time with millimetre accuracy. The PSI method is especially suited for urban areas, because of the high density of good radar back scatterers in these locations. For high-resolution SAR data, such as those acquired by the TerraSAR-X mission, millions of such so-called Persistent Scatterer (PS) points and their deformation time series can be obtained. The presentation, evaluation, and interpretation of such data is still a challenge. The here presented research contributes to the question of how the joint analysis of many PS points and their time series can be used to infer the underlying causes of the deformation. The investigation of such a field of time series helps in the understanding of temporal and spatial patterns in movements. A distinction is made between the analysis of large-scale areas and the consideration of points on individual buildings. For wide-area deformations, such as those caused by underground constructions, mining activities or by undermining groundwater flow, adapted methods from meteorology, interpolation and decomposition procedures of different observation geometries are presented and discussed. For the monitoring of individual structures, such as single buildings, methods were developed that combine SAR data and geo-data from other sources, such as Airborne Laser Scanning (ALS) data and crowd sourced building circumferences. It can be shown that by grouping PS points that have correlated motion patterns, a building can be segmented into its statically independently moving elements. To achieve such a clustering in a robust way, so it can be applied to different data sources, a non-linear dimension reduction based on a hybrid distance metric is introduced. The results from such a clustering can then be integrated into detailed 3D models, such as those available for Building Information Modeling (BIM) based construction processes, and thus offer the possibility of a continuous and efficient structural monitoring of a building. Often PSI results have to be communicated to experts from non-SAR affine fields such as civil- and geo- engineering for interpretation, which can be challenging without specialized software. For this purpose, exemplary web portals are presented here, that allow PSI results to be displayed interactively. Such platforms are addressing the specific complexity of PSI data, so that informed decisions can be made. The utility of an ensemble evaluation of many PSI time series can be demonstrated, as it proves beneficial in wide-area processes. Motion patterns become identifiable and their spatial propagation can undergo analysis. When considering PS points on single buildings, a grouping of points based on their deformation patterns leads to redundant measurement and segments a structure into its independently moving parts. This segmentation can then be integrated into existing 3D building models and industry standards, signifying an important advancement towards automated and city-wide risk assessment of buildings. Web-based analysis platforms, specifically tailored for the SAR data, serve as a decision support system (DSS) and aid in sharing the findings with non-SAR experts.