Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery

Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet.First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science

LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas

Recently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurring increases with the ever-growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance.Comment: Accepted at Electronic Imaging 201

6-(4-Fluorophenyl)-8-phenyl-2,3-dihydro-4H-imidazo[5,1-b][1,3]thiazin-4-one: an unusual [6-5] fused-ring system

The title compound, C₁₈H₁₃FN₂OS, is the first structural example of a [6-5] fused ring incorporating the 2,3-dihydro-4H-imidazo[5,1-b][1,3]thiazin-4-one molecular scaffold. The six-membered 2,3-dihydro-1,3-thiazin-4-one ring adopts an envelope conformation, with the S-CH₂ C atom displaced by 0.761 (2) Å from the five-atom plane (all within 0.05 Å of the mean plane). The imidazole ring is planar. The phenyl ring is twisted from coplanarity with the imidazole ring by 23.84 (5)° and the 4-fluorophenyl ring is twisted by 53.36 (6)°, due to a close C(aryl)-H...O=C contact with the thiazin-4-one carbonyl O atom. The primary intermolecular interaction involves a CH₂ group with the F atom [C...F = 3.256 (2) Å and C-H...F = 137°]

Optimal Taxation of Risky Human Capital

In a model with ex-ante homogenous households, earnings risk and a general earnings function, we derive the optimal linear labor tax rate and optimal linear education subsidies. The optimal income tax trades off social insurance against incentives to work and to invest in human capital. Education subsidies are not used for social insurance, but are only targeted at off-setting the distortions of the labor tax and internalizing a fiscal externality. Both optimal education subsidies and tax rates increase if labor and education are more complementary, since education subsidies indirectly lower labor tax distortions by stimulating labor supply. Optimal education subsidies (taxes) also correct non-tax distortions arising from missing insurance markets. Education subsidies internalize a positive (negative) fiscal externality if there is underinvestment (overinvestment) in education due to risk. Education policy unambiguously allows for more social insurance if education is a risky activity. However, if education hedges against labor market risk, optimal tax rates could be lower than without education subsidies

Reversible Embedding to Covers Full of Boundaries

In reversible data embedding, to avoid overflow and underflow problem, before data embedding, boundary pixels are recorded as side information, which may be losslessly compressed. The existing algorithms often assume that a natural image has little boundary pixels so that the size of side information is small. Accordingly, a relatively high pure payload could be achieved. However, there actually may exist a lot of boundary pixels in a natural image, implying that, the size of side information could be very large. Therefore, when to directly use the existing algorithms, the pure embedding capacity may be not sufficient. In order to address this problem, in this paper, we present a new and efficient framework to reversible data embedding in images that have lots of boundary pixels. The core idea is to losslessly preprocess boundary pixels so that it can significantly reduce the side information. Experimental results have shown the superiority and applicability of our work

Synthesis and characterization of bis(eta(5)-1,2,3,4,5-pentamethylcyclopentadienyl)(eta(3)-1-phenylallyl)lanthanum center dot tetrahydrofuran

The title compound has been prepared from Cp-2*LaCl2K(THF)(2) and 1-PhC3H4K-(THF)(0.5) in THF suspension, forming yellow single crystals from hexane solution which were characterized in solid state and in solution by elementary analysis, IR, C-13- and variable temperature H-1-NMR spectroscopy and a crystal structure determination. Space group P1, Z = 2, T = 130 K, a = 8.595(1), b = 10.770(1), c = 17.903(5) angstrom, alpha = 93.54(1)degrees, beta = 98.30(1)degrees, gamma = 112.42(1)degrees, R = 0.0249

An international collaborative research network helps to design climate robust rice systems

Rice is the world's most important staple food. Although mainly produced in Asia (91%), it is consumed on all continents and its global importance and consumption is increasing. The limited scope to expand production areas coupled with increasing resource constraints (mainly the lack of or competing demands for land and water) make it difficult to meet necessary production increases. Climate change in terms of increasing temperatures, more frequent droughts, anticipated loss of productive estuaries due to rising sea levels, more frequent and severe storms and rising CO2 levels further compounds these problems. This constitutes a huge challenge for science, policy and farmers. The provision of effective solutions is complex due to the spatialtemporal dimensions that must be integrated when setting research, policy and management priorities. These challenges have motivated us to form a Community of Practice (CoP) of concerned scientists. We formed this CoP around the central theme of simulation modelling as a technology that allows integration of discipline-based component science across space and time. We also use modelling as an engagement tool with stakeholders and to connect seemingly disparate scientific disciplines. Here we put our Research for Development (R4D) activities into context and report on some of the research efforts that our CoP is currently involved in. In our quest to design locally-adapted, profitable and sustainable, climate-robust rice-based cropping systems, we welcome input from the wider, global R4D community. (Résumé d'auteur