Journal: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Abbreviation

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Publisher

Copernicus

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ISSN

1682-1750
2194-9034
1682-1777

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Publications 1 - 10 of 76
  • 3D Reconstruction with a Collaborative Approach Based on Smartphones and a Cloud-Based Server
    Item type: Conference Paper
    Nocerino, Erica; Poiesi, Fabio; Locher, Alex; et al. (2017)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • Evaluation of Airborne Image Velocimetry Approaches Using Low-Cost Uavs in Riverine Environments
    Item type: Conference Paper
    Ioli, F; Pinto, Livio; Passoni, Daniele; et al. (2020)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Traditional flow velocity measurements in natural environments require contact with the fluid and are usually costly, time-consuming and, sometimes, even dangerous. Particle Image Velocimetry allows the flow velocity field to be remotely characterized from the shift of intensity patterns of sub-image areas in at least two video frames with a known time lag. Recently, Airborne Image Velocimetry has enabled the surface velocity field of large-scale water bodies to be determined by applying Particle Image Velocimetry on videos recorded by cameras mounted on unmanned aerial vehicles. This work presents a comparison of three Airborne Image Velocimetry approaches: BASESURV, Fudaa-LSPIV and RIVeR. For the evaluation, two nadiral videos were acquired with a low-cost quadcopter. The first was recorded under low flow and seeded conditions, the second during a flood event. According to the results obtained, BASESURV is an accurate and complete research oriented approach but it is time-consuming and neither a graphical interface nor documentation are yet provided. Fudaa-LSPIV is a well-developed software package, with a user-friendly graphical interface and good documentation. However it lacks some features and the source code is closed. RIVeR may be suitable for real time monitoring thanks to the rectification of velocity vectors only. Overall, all the codes are found to be effective in performing Airborne Image Velocimetry in riverine environments. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
  • Crowd4Ems: A crowdsourcing platform for gathering and geolocating social media content in disaster response
    Item type: Conference Paper
    Ravi Shankar, Amudha; Fernandez-Marquez, Jose Luis; Pernici, Barbara; et al. (2019)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. These initiatives face two main challenges: (1) most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders, and (2) they lack tools to manage volunteers contributions and aggregate them in order to ensure high quality and reliable results. This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers contributions. High precision geolocation is achieved by combining data mining techniques for estimating the location of photos and videos from social media, and crowdsourcing for the validation and/or improvement of the estimated location. The evaluation of the proposed approach is carried out using data related to the Amatrice Earthquake in 2016, coming from Flickr, Twitter and Youtube. A common data set is analyzed and geolocated by both the volunteers using the proposed platform and a group of experts. Data quality and data reliability is assessed by comparing volunteers versus experts results. Final results are shown in a web map service providing a global view of the information social media provided about the Amatrice Earthquake event.
  • Bundle adjustment with polynomial point-to-camera distance dependent corrections for underwater photogrammetry
    Item type: Conference Paper
    Nocerino, Erica; Menna, Fabio; Grün, Armin (2021)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Uncontrolled refraction of optical rays in underwater photogrammetry is known to reduce its accuracy potential. Several strategies have been proposed aiming at restoring the accuracy to levels comparable with photogrammetry applied in air. These methods are mainly based on rigours modelling of the refraction phenomenon or empirical iterative refraction corrections. The authors of this contribution have proposed two mitigation strategies of image residuals systematic patterns in the image plane: (i) empirical weighting of image observations as function of their radial position; (ii) iterative look-up table corrections computed in a squared grid. Here, a novel approach is developed. It explicitly takes into account the object point-to-camera distance dependent error introduced by refraction in multimedia photogrammetry. A polynomial correction function is iteratively computed to correct the image residuals clustered in radial slices in the image plane as function of the point-to-camera distance. The effectiveness of the proposed method is demonstrated by simulations that allow to: (i) separate the geometric error under investigation from other effects not easily modellable and (ii) have reliable reference data against which to assess the accuracy of the result.
  • Image Quality Improvements in Low-Cost Underwater Photogrammetry
    Item type: Conference Paper
    Neyer, Fabian; Nocerino, Erica; Grün, Armin (2019)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    This study presents an evaluation of a cheap consumer-grade camera used for modelling a coral reef section. We evaluate the quality of a reconstructed coral reef using GoPro cameras and a high-end camera with data from an actual coral reef dataset. We also investigate components of the processing pipeline (like image quality) separate from the final results. Because our GoPro images suffer from severe chromatic aberration, we apply different image pre-processing steps to improve their quality and show its effects on the reconstructed object points. Bundle adjustment is carried out as free networks in all cases, with a follow-up rigid 3D Helmert transformation onto a geodetic control network, carried out to define the common datum and to remove the bias from the free network results.
  • The potential of sentinel-1 data to supplement high resolution earth observation data for monitoring green areas in cities
    Item type: Conference Paper
    Iglseder, Anna; Bruggisser, Moritz; Dostálová, Alena; et al. (2021)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Green areas play an important role within urban agglomerations due to their impact on local climate and their recreation function. For detailed monitoring, frameworks like the flora fauna habitat (FFH) classification scheme of the European Union's Habitat Directive are broadly used. By date, FFH classifications are mostly expert-based. Within this study, a data-driven approach for FFH classification is tested. For two test areas in the municipality of Vienna, ALS point cloud data are used to derive predictor variables like terrain features, vegetation structure and potential insulation as well as reflection properties from full waveform analysis on a 1m grid. In addition, Sentinel-1 C-Band time series data are used to increase the temporal resolution of the predicting features and to add phenological characteristics. For two 1.3×1.3km test tiles, random forest classifiers are trained using different combinations (ALS, SAR, ALS+SAR) of input features. For all model test runs, the combination of ALS and SAR input features lead to best prediction accuracies when applied on test data.
  • Low-cost mapping of forest under-storey vegetation using spherical photogrammetry
    Item type: Conference Paper
    Murtiyoso, Arnadi; Hristova, Hristina; Rehush, Nataliia; et al. (2022)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    This paper is an attempt to respond to the growing need and demand of 3D data in forestry, especially for 3D mapping. The use of terrestrial laser scanners (TLS) dominates contemporary literature for under-storey vegetation mapping as this technique provides precise and easy-to-use solutions for users. However, TLS requires substantial investments in terms of device acquisition and user training. The search for and development of low-cost alternatives is therefore an interesting field of inquiry. Here, we use low-cost 360° cameras combined with spherical photogrammetric principles for under-storey vegetation mapping. While we fully assume that this low-cost approach will not generate results on par with either TLS or classical close-range photogrammetry, its main aim is to investigate whether this alternative is sufficient to meet the requirements of forest mapping. In this regard, geometric analyses were conducted using both TLS and close-range photogrammetry as comparison points. The diameter at breast height (DBH), a parameter commonly used in forestry, was then computed from the 360° point cloud using three different methods to determine if a similar order of precision to the two reference datasets can be obtained. The results show that 360° cameras were able to generate point clouds with a similar geometric quality as the references despite their low density, albeit with a significantly higher amount of noise. The effect of the noise is also evident in the DBH computation, where it yielded an average error of 3.5 cm compared to both the TLS and close-range photogrammetry.
  • Initial assessment on the use of state-of-The-Art nerf neural network 3d reconstruction for heritage documentation
    Item type: Conference Paper
    Murtiyoso, Arnadi; Grussenmeyer, Pierre (2023)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    In recent decades, photogrammetry has re-emerged as a viable solution for heritage documentation. Developments in various computer vision methods have helped photogrammetry to compete against the laser scanning technology, eventually becoming complementary solutions for the purpose of heritage recording. In the last few years, artificial intelligence (AI) has progressively entered various domains including 3D reconstruction. The Neural Radiance Fields (NeRF) method renders a 3D scene from a series of overlapping images, similar to photogrammetry. However, instead of relying on geometrical relations between the image and world spaces, it uses neural networks to recreate the so-called radiance fields. The result is a significantly faster method of recreating 3D scenes. While not designed to generate 3D models, simple computer graphics methods can be used to convert these recreated radiance fields into the familiar point cloud. In this paper, we implemented the Nerfacto architecture to recreate two instances of heritage objects and then compared them to traditional photogrammetric multi-view stereo (MVS). While the initial hypothesis posits that NeRF is not yet capable to reach the level of accuracy and density achieved by MVS as can be observed in the results, NeRF nevertheless shows a great potential due to its fractionally faster processing speed.
  • Monitoring coral growth - The dichotomy between underwater photogrammetry and geodetic control network
    Item type: Conference Paper
    Neyer, Fabian; Nocerino, Erica; Grün, Armin (2018)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Creating 3-dimensional (3D) models of underwater scenes has become a common approach for monitoring coral reef changes and its structural complexity. Also in underwater archeology, 3D models are often created using underwater optical imagery. In this paper, we focus on the aspect of detecting small changes in the coral reef using a multi-temporal photogrammetric modelling approach, which requires a high quality control network. We show that the quality of a good geodetic network limits the direct change detection, i.e., without any further registration process. As the photogrammetric accuracy is expected to exceed the geodetic network accuracy by at least one order of magnitude, we suggest to do a fine registration based on a number of signalized points. This work is part of the Moorea Island Digital Ecosystem Avatar (IDEA) project that has been initiated in 2013 by a group of international researchers (https://mooreaidea.ethz.ch/).
  • 2D to 3D Label Propagation for the Semantic Segmentation of Heritage Building Point Clouds
    Item type: Conference Paper
    Pellis, Eugenio; Murtiyoso, Arnadi; Masiero, Andrea; et al. (2022)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    During the last decade, the use of semantic models of 3D buildings and structures kept growing, fostered in particular by the spread of Building Information Models (BIMs), becoming quite popular in several civil engineering and geomatics applications. Nevertheless, semantic model production usually requires quite a lot of human interaction, which may result in quite long and annoying procedures for human operators. The production of 3D semantic models of buildings often takes advantage of already available 3D reconstructions of the considered objects. Given the ever increasing resolution of 3D reconstructions, obtained thanks to the recently developed laser scanners and photogrammetric software, the availability of tools for supporting the automatic or semi-automatic generation of semantic models represents a key step for easing and speeding up the process of semantic model production. In particular, the correct semantic interpretation of the different parts of a 3D point cloud, can be seen as the basic step for the production of a BIM model. The most frequently used methods for point cloud semantic segmentation can be separated in two categories: those directly segmenting the point clouds and those based on the ancillary semantic segmentation of images representing the object of interest, then transferring back the segmentation results to the point cloud. This work focuses on the latter method, considering more specifically the application of heritage building semantic segmentation. To be more specific, this paper investigates the semantic segmentation performance on a set of four heritage buildings, obtained first applying deep-learning based image semantic segmentation and then propagating back the semantic information to the point cloud by means of a voting strategy. The obtained results are quite encouraging, motivating future investigations on improvements of this strategy, in particular when including more buildings in the considered dataset.
Publications 1 - 10 of 76