183 research outputs found
Rapid Disaster Analysis based on SAR Techniques
Due to all-day and all-weather capability spaceborne SAR is a valuable means for rapid mapping during and after disaster. In this
paper, three change detection techniques based on SAR data are discussed: (1) initial coarse change detection, (2) flooded area
detection, and (3) linear-feature change detection. The 2011 Tohoku Earthquake and Tsunami is used as case study, where
earthquake and tsunami events provide a complex case for this study. In (1), pre- and post-event TerraSAR-X images are coregistered
accurately to produce a false-color image. Such image provides a quick and rough overview of potential changes, which is
useful for initial decision making and identifies areas worthwhile to be analysed further in more depth. In (2), the post-event
TerraSAR-X image is used to extract the flooded area by morphological approaches. In (3), we are interested in detecting changes of
linear shape as indicator for modified man-made objects. Morphological approaches, e.g. thresholding, simply extract pixel-based
changes in the difference image. However, in this manner many irrelevant changes are highlighted, too (e.g., farming activity,
speckle). In this study, Curvelet filtering is applied in the difference image not only to suppress false alarms but also to enhance the
change signals of linear-feature form (e.g. buildings) in settlements. Afterwards, thresholding is conducted to extract linear-shaped
changed areas. These three techniques mentioned above are designed to be simple and applicable in timely disaster analysis. They
are all validated by comparing with the change map produced by Center for Satellite Based Crisis Information, DLR
PERSISTENT SCATTERER AIDED FACADE LATTICE EXTRACTION IN SINGLE AIRBORNE OPTICAL OBLIQUE IMAGES
We present a new method to extract patterns of regular facade structures from single optical oblique images. To overcome the missing
three-dimensional information we incorporate structural information derived from Persistent Scatter (PS) point cloud data into our
method. Single oblique images and PS point clouds have never been combined before and offer promising insights into the compatibility
of remotely sensed data of different kinds. Even though the appearance of facades is significantly different, many characteristics of the
prominent patterns can be seen in both types of data and can be transferred across the sensor domains. To justify the extraction based
on regular facade patterns we show that regular facades appear rather often in typical airborne oblique imagery of urban scenes. The
extraction of regular patterns is based on well established tools like cross correlation and is extended by incorporating a module for
estimating a window lattice model using a genetic algorithm. Among others the results of our approach can be used to derive a deeper
understanding of the emergence of Persistent Scatterers and their fusion with optical imagery. To demonstrate the applicability of the
approach we present a concept for data fusion aiming at facade lattices extraction in PS and optical data
SLAM for Indoor Mapping of Wide Area Construction Environments
Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring
Land subsidence hazard in iran revealed by country-scale analysis of sentinel-1 insar
Many areas across Iran are subject to land subsidence, a sign of exceeding stress due to the over-extraction of groundwater during the past decades. This paper uses a huge dataset of Sentinel-1, acquired since 2014 in 66 image frames of 250×250km, to identify and monitor land subsidence across Iran. Using a two-step time series analysis, we first identify subsidence zones at a medium scale of 100m across the country. For the first time, our results provide a comprehensive nationwide map of subsidence in Iran and recognize its spatial distribution and magnitude. Then, in the second step of analysis, we quantify the deformation time series at the highest possible resolution to study its impact on civil infrastructure. The results spots the hazard posed by land subsidence to different infrastructure. Examples of road and railways affected by land subsidence hazard in Tehran and Mashhad, two of the most populated cities in Iran, are presented in this study
Exploring cloud-based platforms for rapid insar time series analysis
The idea of near real-time deformation analysis using Synthetic Aperture Radar (SAR) data as a response to natural and anthropogenic disasters has been an interesting topic in the last years. A major limiting factor for this purpose has been the non-availability of both spatially and temporally homogeneous SAR datasets. This has now been resolved thanks to the SAR data provided by the Sentinel-1A/B missions, freely available at a global scale via the Copernicus program of the European Space Agency (ESA). Efficient InSAR analysis in the era of Sentinel demands working with cloud-based platforms to tackle problems posed by large volumes of data. In this study, we explore a variety of existing cloud-based platforms for Multioral Interferometric SAR (MTI) analysis and discuss their opportunities and limitations
Refinement type contracts for verification of scientific investigative software
Our scientific knowledge is increasingly built on software output. User code
which defines data analysis pipelines and computational models is essential for
research in the natural and social sciences, but little is known about how to
ensure its correctness. The structure of this code and the development process
used to build it limit the utility of traditional testing methodology. Formal
methods for software verification have seen great success in ensuring code
correctness but generally require more specialized training, development time,
and funding than is available in the natural and social sciences. Here, we
present a Python library which uses lightweight formal methods to provide
correctness guarantees without the need for specialized knowledge or
substantial time investment. Our package provides runtime verification of
function entry and exit condition contracts using refinement types. It allows
checking hyperproperties within contracts and offers automated test case
generation to supplement online checking. We co-developed our tool with a
medium-sized (3000 LOC) software package which simulates
decision-making in cognitive neuroscience. In addition to helping us locate
trivial bugs earlier on in the development cycle, our tool was able to locate
four bugs which may have been difficult to find using traditional testing
methods. It was also able to find bugs in user code which did not contain
contracts or refinement type annotations. This demonstrates how formal methods
can be used to verify the correctness of scientific software which is difficult
to test with mainstream approaches
Planck 2015 results. XIV. Dark energy and modified gravity
We study the implications of Planck data for models of dark energy (DE) and modified gravity (MG), beyond the cosmological constant scenario. We start with cases where the DE only directly affects the background evolution, considering Taylor expansions of the equation of state, principal component analysis and parameterizations related to the potential of a minimally coupled DE scalar field. When estimating the density of DE at early times, we significantly improve present constraints. We then move to general parameterizations of the DE or MG perturbations that encompass both effective field theories and the phenomenology of gravitational potentials in MG models. Lastly, we test a range of specific models, such as k-essence, f(R) theories and coupled DE. In addition to the latest Planck data, for our main analyses we use baryonic acoustic oscillations, type-Ia supernovae and local measurements of the Hubble constant. We further show the impact of measurements of the cosmological perturbations, such as redshift-space distortions and weak gravitational lensing. These additional probes are important tools for testing MG models and for breaking degeneracies that are still present in the combination of Planck and background data sets. All results that include only background parameterizations are in agreement with LCDM. When testing models that also change perturbations (even when the background is fixed to LCDM), some tensions appear in a few scenarios: the maximum one found is \sim 2 sigma for Planck TT+lowP when parameterizing observables related to the gravitational potentials with a chosen time dependence; the tension increases to at most 3 sigma when external data sets are included. It however disappears when including CMB lensing
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