489 research outputs found
Preface: Technical commission II
No full textISSN:1682-1750ISSN:2194-9034ISSN:1682-1777ISSN:2194-9034ISSN:1682-177
"The end of national models? Integration courses and citizenship trajectories in Europe"
Get PDFSeveral European countries have recently introduced or are planning to introduce citizenship trajectories (voluntary or obligatory inclusion programs for recent immigrants) or citizen integration tests (tests one should pass to be able and acquire permanent residence or state citizenship). Authors like Joppke claim this is an articulation of a more general shift towards the logic of assimilation (and away from a multicultural agenda) in integration policy paradigms of European States. Integration policies would even be converging in such a fashion that it would no longer make sense to think in terms of national models for immigrant integration. One cannot deny the empirical fact of diffusion of civic integration policies throughout Europe. This paper claims there is, however, still sufficient distinctiveness between immigrant integration policies in order to continue and use an analytical framework which distinguishes national models
Using Passive Multi-Modal Sensor Data for Thermal Simulation of Urban Surfaces
Get PDFThis paper showcases an integrated workflow hinged on passive airborne multi-modal sensor data for the simulation of the thermal behavior of built-up areas with a focus on urban heat islands. The geometry of the underlying parametrized model, or digital twin, is derived from high-resolution nadir and oblique RGB, near-infrared and thermal infrared imagery. The captured bitmaps get photogrammetrically processed into comprehensive surface models, terrain, dense 3D point clouds and true-ortho mosaics. Building geometries are reconstructed from the projected point sets with procedures presupposing outlining, analysis of roof and fac¸ade details, triangulation, and texturing mapping. For thermal simulation, the composition of the ground is determined using supervised machine learning based on a modified multi-modal DeepLab v3+ architecture. Vegetation is retrieved as individual trees and larger tree regions to be added to the meshed terrain. Building materials are assigned from the available visual, infrared and surface planarity information as well as publicly available references. With actual weather data, surface temperatures can be calculated for any period of time by evaluating conductive, convective, radiative and emissive energy fluxes for triangular layers congruent to the faces of the modeled scene. Results on a sample dataset of the Moabit district in Berlin, Germany, showed the ability of the simulator to output surface temperatures of relatively large datasets efficiently. Compared to the thermal infrared images, several insufficiencies in terms of data and model caused occasional deviations between measured and simulated temperatures. For some of these shortcomings, improvement suggestions within future work are presented
Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines
No full textCanopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.
The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).
The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":
0: Open land (OL) [#440154]
1: Scrub (S) [#404387]
2: Young regenerating forest (YRF) [#29788e]
3: Low density forest (LDF) [#22a884]
4: Medium density forest (MDF) [#7ad251]
5: High density forest (HDF) [#fde725]
10: Oil palm [#fcffa4]
11: Coconut [#a4feff]
50: Urban [#fa0000]
255: No data
Citation: Use of these data require citation of this dataset and the original research articles. These citations are as follows:
Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.
Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. Remote Sensing of Environment, 261, 112479.
Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448The project received funding from Barry Callebaut Sourcing AG, as part of a Research Project Agreement
Depth-Aware Panoptic Segmentation
Get PDFPanoptic segmentation unifies semantic and instance segmentation and thus delivers a semantic class label and, for so-called thing classes, also an instance label per pixel. The differentiation of distinct objects of the same class with a similar appearance is particularly challenging and frequently causes such objects to be incorrectly assigned to a single instance. In the present work, we demonstrate that information on the 3D geometry of the observed scene can be used to mitigate this issue: We present a novel CNN-based method for panoptic segmentation which processes RGB images and depth maps given as input in separate network branches and fuses the resulting feature maps in a late fusion manner. Moreover, we propose a new depth-aware dice loss term which penalises the assignment of pixels to the same thing instance based on the difference between their associated distances to the camera. Experiments carried out on the Cityscapes dataset show that the proposed method reduces the number of objects that are erroneously merged into one thing instance and outperforms the method used as basis by +2.2% in terms of panoptic quality
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
Get PDFIncreasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO2 emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results
Nerium oleander L.
No full text66. Nerium oleander L., Sp. Pl.: 209. 1753 [cited in Nat. Hist. II(4): 389. 1775]. Notes. – A specimen in the Java herbarium is in L (L-898.111-57). It represents material of Plumeria rubra L. (see Nat. Hist. II(2): 178. 1774) (Fig. 2). Two leaves and an inflorescence are mounted with a mid-18th century pot, so far only known from this sheet (G. Thijsse, pers. comm. to the first author). Another specimen without provenance is present in the Thunberg herbarium (UPS-THUNB n° 6128).Published as part of Wijnands, Dirk Onno, Heniger, Johannes, Veldkamp, Jan Frederik, Fumeaux, Nicolas & Callmander, Martin W., 2017, The botanical legacy of Martinus Houttuyn (1720 - 1798) in Geneva, pp. 155-198 in Candollea 72 (1) on page 181, DOI: 10.15553/c2017v721a11, http://zenodo.org/record/572188
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