Nowadays, integrated BIM-GIS applications are gaining more attention in projects related to structures and infrastructures. On the other hand, advanced tools able to simultaneously exploit advantages of both GIS and BIM are not available yet. Applications are carried out with different software, resulting in an inevitable information loss in the continuous file conversion and transfer between different software packages. The aim of this paper was to create an integrated BIM-GIS using Autodesk InfraWorks, combining the advantages of parametric modeling with geospatial datasets, and testing pros and cons of software for integrated BIM-GIS processing. The aim was to obtain a BIM-GIS model at the scale of a medium-size city. Results demonstrate that existing geospatial datasets allow one to generate preliminary models, which however require extensive manual editing to become tools for parametric modeling and simulation of infrastructures.
A novel method for change detection from sequences of terrestrial (close-range) images is illustrated and discussed. The method was developed to analyze changes over a very long period of time (years). The proposed case study is the monitoring project of a rockfall, in which the first image was acquired in 2011, i.e. 7 years ago. The method does not rely on a fixed camera, so that the same camera can be removed and used for other applications. A procedure able to recover the alignment of the different images is required before running a image-to-image comparison for change detection. In the second step of the process, normalized multispectral cross correlation analysis with additional vegetation filtering is used to determine variations in the scene.
A large development of downstream services is expected to be stimulated starting from earth observations (EO) datasets acquired by Copernicus satellites. An important challenge connected with the availability of downstream services is the possibility for their integration in order to create innovative applications with added values for users of different categories level. At the moment, the world of geo-information (GI) is extremely heterogeneous in terms of standards and formats used, thus preventing a facilitated access and integration of downstream services. Indeed, different users and data providers have also different requirements in terms of communication protocols and technology advancement. In recent years, many important programs and initiatives have tried to address this issue even on trans-regional and international level (e.g. INSPIRE Directive, GEOSS, Eye on Earth and SEIS). However, a lack of interoperability between systems and services still exists. In order to facilitate the interaction between different downstream services, a new architectural approach (developed within the European project ENERGIC OD) is proposed in this paper. The brokering-oriented architecture introduces a new mediation layer (the Virtual Hub) which works as an intermediary to bridge the gaps linked to interoperability issues. This intermediation layer de-couples the server and the client allowing a facilitated access to multiple downstream services and also Open Data provided by national and local SDIs. In particular, in this paper an application is presented integrating four services on the topic of agriculture: (i) the service given by Space4Agri (providing services based on MODIS and Landsat data); (ii) Gicarus Lab (providing sample services based on Landsat datasets) and (iii) FRESHMON (providing sample services for water quality) and services from a several regional SDIs.
Starting from June 2015, Sentinel-2A is delivering high resolution optical images (ground resolution up to 10 meters) to provide a global coverage of the Earth’s land surface every 10 days. The planned launch of Sentinel-2B along with the integration of Landsat images will provide time series with an unprecedented revisit time indispensable for numerous monitoring applications, in which high resolution multi-temporal information is required. They include agriculture, water bodies, natural hazards to name a few. However, the combined use of multi-temporal images requires an accurate geometric registration, i.e. pixel-to-pixel correspondence for terrain-corrected products. This paper presents an analysis of spatial co-registration accuracy for several datasets of Sentinel-2 and Landsat 8 images distributed all around the world. Images were compared with digital correlation techniques for image matching, obtaining an evaluation of registration accuracy with an affine transformation as geometrical model. Results demonstrate that sub-pixel accuracy was achieved between 10 m resolution Sentinel-2 bands (band 3) and 15 m resolution panchromatic Landsat images (band 8).
Radiometric image normalization is one of the basic pre-processing methods used in satellite time series analysis. This paper presents a new multi-image approach able to estimate the parameters of relative radiometric normalization through a multiple and simultaneous regression with a dataset of a generic number of images. The method was developed to overcome the typical drawbacks of standard one-to-one techniques, where image pairs are independently processed. The proposed solution is based on multi-image pseudo-invariant features incorporated into a unique regression solved via Least Squares. Results for both simulated and real data are presented and discussed.
Remote sensing and space technologies are increasingly called to offer innovative solutions for current challenges induced by climatic and global change. One of the main priorities of the European Space Policy regards the economic independence of the old continent in this sector. In terms of research and innovation this inevitably leads to numerous attempts in having independent market of services that would tackle specific needs of the citizens. Agriculture, for example, is one of the sectors majorly subsidized by European funds on national, regional and local level, with the aim to foster a more productive and sustainable development. Due to a large territorial scale at which agricultural phenomena are observed, and thus the spatial resolution required, it is also one of the main sectors that has been monitored from space over the past 30 years. In fact, one of the main missions of USA Landsat satellites was to provide a continuous and systematic overview of the globe for the purposes of an effective monitoring of the environment. This paper represents an overview of the ongoing initiatives in Space research done for the field of agriculture and landscape monitoring. In particular, the paper looks into the future possibilities that will be offered by full, open and free-of-charge data arriving from ongoing Copernicus missions and the contribution of Sentinel satellites to the agricultural sector.
The commercial market offers several software packages for the registration of remotely sensed data through standard
one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the
interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called
Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards
multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar
(COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust
workflow without initial approximations, user’s interaction or limitation in spatial/spectral data size. The validation
highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and
radar imagery.
Very high resolution (VHR) satellite images have the potential for revealing changes occurred overtime with a superior level of detail. However, their use for metric purposes requires accurate geo-localization with ancillary DEMs and GCPs to achieve sub-pixel terrain correction, in order to obtain images useful for mapping applications. Change detection with a time series of VHS images is not a simple task because images acquired with different off-nadir angles have a lack of pixel-to-pixel image correspondence, even after accurate geo-correction. This paper presents a procedure for automatic change detection able to deal with variable off-nadir angles. The case study concerns the identification of damaged buildings from pre- and post-event images acquired on the historic center of L’Aquila (Italy), which was struck by an earthquake in April 2009. The developed procedure is a multi-step approach where (i) classes are assigned to both images via object-based classification, (ii) an initial alignment is provided with an automated tile-based rubber sheeting interpolation on the extracted layers, and (iii) change detection is carried out removing residual mis-registration issues resulting in elongated features close to building edges. The method is fully automated except for some thresholds that can be interactively set to improve the visualization of the damaged buildings. The experimental results proved that damages can be automatically found without additional information, such as digital surface models, SAR data, or thematic vector layers.
A rigorous methodology for mapping thermal and RGB images on three-dimensional (3-D) models of building façades is presented. The developed method differs from most existing approaches because it relies on the use of thermal images coupled with 3-D models derived from terrestrial laser scanning surveying. The primary issue for an accurate texturing is the coregistration of the geometric model of the façade and the thermal images in the same reference system. This task is done by using a procedure standing out from other approaches adopted in current practice, which are mainly based on the independent registration of each image on the basis of homography or space resection techniques. A rigorous photogrammetric orientation of both thermal and RGB images is computed together in a combined bundle adjustment. This solution allows one to have a better control of the quality of the results, especially to reduce errors and artifacts in areas where more images are mosaicked onto the 3-D model. Several products can be obtained: 3-D triangulated textured models or raster products like orthophotos, having the temperature as radiometric value. The proposed approach is tested on different buildings of Politecnico di Milano University. Applications demonstrated the performance of the procedure and its technical applicability in routine thermal surveys.
Nowadays several thermal cameras capture images based on a pinhole camera model. This paper shows how multiple images of flat-like objects or 3D bodies can be mapped and mosaicked with a mathematical formulation between image and object spaces. This work demonstrates that both geometric and radiometric parts need proper mathematical models that allow the user to obtain a global product (orthophotos or 3D models) where accurate and detailed photogrammetric models and thermal images are registered in order to combine geometry and thermal information.
This paper presents the use of laser tracking technology for structure monitoring. In this field the use of this precise instrument is innovative and therefore new investigations are needed for civil structures, especially for applications carried out during unstable environmental conditions. On the other hand, as laser trackers are today very used in industrial applications aimed at collecting data at high speed with precisions superior to ±0.05 mm, they seem quite promising for those civil engineering applications where numerous geodetic tools, often coupled with mechanical and electrical instruments, are usually used to inspect structure movements. This work illustrates three real civil engineering monitoring applications where laser tracking technology was used to detect object movements. The first one is a laboratory testing for the inspection of a beam (bending moment and shear). The second experiment is the stability inspection of a bridge. The last experiment is one of the first attempts where laser trackers tried to substitute traditional high precision geometric leveling for monitoring an important historical building: the Cathedral of Milan. The achieved results, pro and contra along with some practical issues are described.
At present there is a lack of commercial software packages able to perform differential temperature gradient analysis. This is an innovative and fundamental tool to speed up the recognition of thermal anomalies revealing finishing damages like detachments.
This paper presents a photogrammetric methodology aimed at mapping IR thermal images on 3D models created with terrestrial laser scanning technology. The attention is focused on building, where a standard RGB texture of the 3D model will coupled to temperature values. Each facade will be then transformed into an orthophoto and processed in a GIS environment to support new thermal analysis. The developed image processing pipeline will be illustrated starting from data acquisition up to data visualization and management.
KEYWORDS: Laser scanners, 3D modeling, Cameras, Photogrammetry, Clouds, Visualization, 3D acquisition, Forensic science, 3D image processing, 3D metrology
Fast documentation of complex scenes where accidents or crimes occurred is fundamental not to lose information for
post-event analyses and lesson learning. Today 3D terrestrial laser scanning and photogrammetry offer instruments
capable of achieving this task. The former allows the fast geometric reconstruction of complex scenes through dense
point clouds. Different kinds of instruments can be used according to the size of the area to survey and to the required
level of details. The latter can be used for both geometric reconstruction and for photo-realistic texturing of laser scans.
While photogrammetry better focuses on small details, laser scanning gives out a more comprehensive view of geometry
of whole crime/accident scene. Both techniques can be used for recording a scene just after a crime or a disaster
occurred, before the area is cleared out to recover regular activities. Visualization of results through an easy-to-use 3D
environment is another import issue to offer useful data to investigators. Here two experiences of crime scene
documentation are proposed.
This paper presents two methodologies able to map a block of IR thermal and RGB images on 3D models derived from
terrestrial laser scanning surveying. Proposed methods stand out from other traditional approaches that are mainly based
on the projection of single images through approximate models. The first method is a rigorous photogrammetric
orientation through a bundle adjustment integrating both RGB and thermal data. Then, another complementary solution
based on the use of a calibrated 'bi-camera' system is illustrated. Both methods allows one to texture building facades
(reconstructed with 3D models) with their temperature values. Finally, several products can be extracted and managed in
different data processing environments: triangulated models to visualize 3D spatial information and to analyze 3D
heating diffusion on the surface; raster datasets (e.g. orthophotos or rectified images) with temperature as radiometric
value. Both approaches were tested on different buildings of Politecnico di Milano University, where a restoration
project of historical and modern facades is currently work in progress.
KEYWORDS: Cameras, Calibration, Distortion, Image restoration, Panoramic photography, High dynamic range imaging, Image processing, Digital imaging, Digital cameras, Image analysis
The identification of a parallelogram in the image plane, along with the knowledge of camera parameters, allows metric
properties to be recovered. This paper presents a methodology capable of estimating a rectifying homography for images
of planar objects without taking any measurement on the world plane. The rectified image will have only an overall scale
ambiguity. The method was implemented to create a photogrammetric package for the estimation of the proposed
projective transformation. This package was also extended to compute homographic transformations with standard
techniques, such as ground control points, rectangles with a known length ratio, and squares. Examples are presented
using synthetic and real data acquired for different purposes, including HDR and panoramic photography. Finally, a
practical test with two photogrammetric staffs was carried out to check the accuracy of the procedure, starting from the
same quantities measured with an optical level.
Automatic image orientation of close-range image blocks is becoming a task of increasing importance in the practice of
photogrammetry. Although image orientation procedures based on interactive tie point measurements do not require any
preferential block structure, the use of structured sequences can help to accomplish this task in an automated way.
Automatic orientation of image sequences has been widely investigated in the Computer Vision community. Here the
method is generally named "Structure from Motion" (SfM), or "Structure and Motion". These refer to the simultaneous
estimation of the image orientation parameters and 3D object points of a scene from a set of image correspondences.
Such approaches, that generally disregard camera calibration data, do not ensure an accurate 3D reconstruction, which is
a requirement for photogrammetric projects. The major contribution of SfM is therefore viewed in the photogrammetric
community as a powerful tool to automatically provide a dense set of tie points as well as initial parameters for a final
rigorous bundle adjustment. The paper, after a brief overview of automatic procedures for close-range image sequence
orientation, will show some characteristic examples. Although powerful and reliable image orientation solutions are
nowadays available at research level, there are certain questions that are still open. Thus the paper will also report some
open issues, like the geometric characteristics of the sequences, scene's texture and shape, ground constraints (control
points and/or free-network adjustment), feature matching techniques, outlier rejection and bundle adjustment models.
Nowadays commercial software able to automatically create an accurate 3D model from any sequence of terrestrial
images is not available. This paper presents a methodology which is capable of processing markerless block of terrestrial
digital images to perform a twofold task: (i) determine the exterior orientation parameters by using a progressive robust
feature-based matching followed by a Least Squares Matching refining and a bundle adjustment; (ii) extract a dense
point-clouds by using a multi-image matching based on diverse image primitives. The final result is an accurate surface
model with characteristics similar to those achievable with range-based sensors. In the whole processing workflow the
natural texture of the object is used, thus images and calibration parameters are the only inputs. The method exploits
Computer Vision and Photogrammetric techniques and combines their advantages in order to automate the process. At
the same time it ensures a precise and reliable solution. To verify the accuracy of the developed methodology, several
comparisons with manual measurements, total station data and 3D laser scanner were also carried out.
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