On Approximate Nonlinear Gaussian Message Passing On Factor Graphs

Factor graphs have recently gained increasing attention as a unified framework for representing and constructing algorithms for signal processing, estimation, and control. One capability that does not seem to be well explored within the factor graph tool kit is the ability to handle deterministic nonlinear transformations, such as those occurring in nonlinear filtering and smoothing problems, using tabulated message passing rules. In this contribution, we provide general forward (filtering) and backward (smoothing) approximate Gaussian message passing rules for deterministic nonlinear transformation nodes in arbitrary factor graphs fulfilling a Markov property, based on numerical quadrature procedures for the forward pass and a Rauch-Tung-Striebel-type approximation of the backward pass. These message passing rules can be employed for deriving many algorithms for solving nonlinear problems using factor graphs, as is illustrated by the proposition of a nonlinear modified Bryson-Frazier (MBF) smoother based on the presented message passing rules

Predicting Building Functions by Fusing Social Media and Remote Sensing Data

Die Funktionen von Gebäuden lassen sich nicht direkt messen, sondern erfordern die Interpretation von Daten. In dieser Arbeit werden drei neue Methoden zur Vorhersage von Gebäudefunktionen vorgestellt, die auf Daten aus sozialen Medien und Fernerkundungsdaten beruhen. Die Methoden basieren auf Ansätzen des maschinellen Lernens und wurden auf kulturell diversifizierten Datensätzen entwickelt und getestet. Die Vorhersage lässt sich durch die Kombination mehrerer Modelle um bis zu 6,9% erhöhen

Influence of ocean warming and acidification on trace metal biogeochemistry

Rising atmospheric CO2 concentrations will have profound effects on atmospheric and hydrographic processes, which will ultimately modify the supply and chemistry of trace metals in the ocean. In addition to an increase in sea surface temperatures, higher CO2 also results in a decrease of seawater pH, known as ocean acidification, with implications for inorganic trace metal chemistry. Furthermore, direct or indirect effects of ocean acidification and ocean warming on marine biota will also affect trace metal biogeochemistry via alteration of biological trace metal uptake rates and metal binding to organic ligands. Currently, we still lack a holistic understanding of the impacts of decreasing seawater pH and rinsing temperatures on different trace metals and marine biota, which complicates projections into the future. Here, we outline how ocean acidification and ocean warming will influence the inputs and cycling of Fe and other biologically relevant trace metals globally, and regionally in high and low latitudes of the future ocean, discuss uncertainties, and highlight essential future research fields

Towards a Formative Measurement Model for Trust

IS research has shown the importance of trust in domains such as e-commerce or technology acceptance. Researchers also emphasize the importance of the identification of factors that influence trust. Unfortunately, the currently dominant reflective measurement does not offer these insights, and thus this contribution aims at developing a formative measurement model for trust. To achieve this, we address three research questions: a) How can trust be measured, considering trust and measurement theory? b) What indicators should be included in a formative measurement model for trust? c) What is the value of a formative measurement of trust compared to a reflective one? Our results show that the formative measurement model offers detailed insights on the impact of single factors influencing trust. We show that in our study, ability affects trust over twice as much other factors such as benevolence or trustor\u27s propensity

Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing?

The fusion of two or more different data sources is a widely accepted technique in remote sensing while becoming increasingly important due to the availability of big Earth Observation satellite data. As a complementary source of geo-information to satellite data, massive text messages from social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Despite the uncontrolled quality: can linguistic features extracted from geo-referenced tweets support remote sensing tasks? This work presents a straightforward decision fusion framework for very high-resolution remote sensing images and Twitter text messages. We apply our proposed fusion framework to a land-use classification task - the building function classification task - in which we classify building functions like commercial or residential based on linguistic features derived from tweets and remote sensing images. Using building tags from OpenStreetMap (OSM), we labeled tweets and very high-resolution (VHR) images from Google Maps. We collected English tweets from San Francisco, New York City, Los Angeles, and Washington D.C. and trained a stacked bi-directional LSTM neural network with these tweets. For the aerial images, we predicted building functions with state-of-the-art Convolutional Neural Network (CNN) architectures fine-tuned from ImageNet on the given task. After predicting each modality separately, we combined the prediction probabilities of both models building-wise at a decision level. We show that the proposed fusion framework can improve the classification results of the building type classification task. To the best of our knowledge, we are the first to use semantic contents of Twitter messages and fusing them with remote sensing images to classify building functions at a single building level

Using social media images for building function classification

Urban land use on a building instance level is crucial geo-information for many applications yet challenging to obtain. Steet-level images are highly suited to predict building functions as the building façades provide clear hints. Social media image platforms contain billions of images, including but not limited to street perspectives. This study proposes a filtering pipeline to yield high-quality, ground-level imagery from large-scale social media image datasets to cope with this issue. The pipeline ensures all resulting images have complete and valid geotags with a compass direction to relate image content and spatial objects. We analyze our method on a culturally diverse social media dataset from Flickr with more than 28 million images from 42 cities worldwide. The obtained dataset is then evaluated in the context of a building function classification task with three classes: Commercial, residential, and other. Fine-tuned state-of-the-art architectures yield F1 scores of up to 0.51 on the filtered images. Our analysis shows that the quality of the labels from OpenStreetMap limits the performance. Human-validated labels increase the F1 score by 0.2. Therefore, we consider these labels weak and publish the resulting images from our pipeline and the depicted buildings as a weakly labeled datase

The Growth Response of Two Diatom Species to Atmospheric Dust from the Last Glacial Maximum

Relief of iron (Fe) limitation in the surface Southern Ocean has been suggested as one driver of the regular glacial-interglacial cycles in atmospheric carbon dioxide (CO2). The proposed cause is enhanced deposition of Fe-bearing atmospheric dust to the oceans during glacial intervals, with consequent effects on export production and the carbon cycle. However, understanding the role of enhanced atmospheric Fe supply in biogeochemical cycles is limited by knowledge of the fluxes and ‘bioavailability’ of atmospheric Fe during glacial intervals. Here, we assess the effect of Fe fertilization by dust, dry-extracted from the Last Glacial Maximum portion of the EPICA Dome C Antarctic ice core, on the Antarctic diatom species Eucampia antarctica and Proboscia inermis. Both species showed strong but differing reactions to dust addition. E. antarctica increased cell number (3880 vs. 786 cells mL-1), chlorophyll a (51 vs. 3.9 μg mL-1) and particulate organic carbon (POC; 1.68 vs. 0.28 μg mL-1) production in response to dust compared to controls. P. inermis did not increase cell number in response to dust, but chlorophyll a and POC per cell both strongly increased compared to controls (39 vs. 15 and 2.13 vs. 0.95 ng cell-1 respectively). The net result of both responses was a greater production of POC and chlorophyll a, as well as decreased Si:C and Si:N incorporation ratios within cells. However, E, antarctica decreased silicate uptake for the same nitrate and carbon uptake, while P. inermis increased carbon and nitrate uptake for the same silicate uptake. This suggests that nutrient utilization changes in response to Fe addition could be driven by different underlying mechanisms between different diatom species. Enhanced supply of atmospheric dust to the surface ocean during glacial intervals could therefore have driven nutrient-utilization changes which could permit greater carbon fixation for lower silica utilization. Additionally, both species responded more strongly to lower amounts of direct Fe chloride addition than they did to dust, suggesting that not all the Fe released from dust was in a bioavailable form available for uptake by diatoms

So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, machine learning methods have been developed to estimate the population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of the new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises digital elevation model, local climate zone, land use classifications, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated machine learning-based approaches in the field of population estimation

Influence of trace metal release from volcanic ash on growth of Thalassiosira pseudonana and Emiliania huxleyi

Recent studies demonstrate that volcanic ash has the potential to increase phytoplankton biomass in the open ocean. However, besides fertilizing trace metals such as Fe, volcanic ash contains a variety of potentially toxic metals such as Cd, Cu, Pb, and Zn. Especially in coastal regions closer to the volcanic eruption, where ash depositions can be very high, toxic effects are possible. Here we present the first results of laboratory experiments, showing that trace metal release from different volcanic materials can have both fertilizing and toxic effects on marine phytoplankton in natural coastal seawater. The diatom Thalassiosira pseudonana generally showed higher growth rates in seawater that was in short contact with volcanic ash compared to the controls without ash addition. In contrast to that, the addition of volcanic ash had either no effect or significantly decreased the growth rate of the coccolithophoride Emiliania huxleyi. It was not possible to attribute the effects to single trace metals, however, our results suggest that Mn plays an important role in regulating the antagonistic and synergistic effects of the different trace metals. This study shows that volcanic ash can lead to changes in the phytoplankton species composition in the high fall-out area of the surface ocean. Highlights: ► We tested the effect of volcanic ash on growth of T. pseudonana and E. huxleyi ► Volcanic ash increased growth of T. pseudonana but not of E. huxleyi ► Mn seems important to regulate the effects of different trace metals from the ash ► Volcanic eruptions have the potential to change phytoplankton community structure

Zooming into Uncertainties: Towards Fusing Multi Zoom Level Imagery for Urban Land Use Segmentation

Urban land use prediction is an ill-posed problem from a remote sensing perspective. Some areas are easy to predict with aerial images, e.g. residential areas or industrial areas, whereas it is nearly impossible to predict land use in dense urban centers with highly mixed land use. In this study, we use a fully convolutional, Bayesian neural network for urban land use segmentation that yields predictions and pixel-wise uncertainty values side-by-side. By adding aleatoric uncertainty to the output of our model, we can assess how much the model benefits from the provided data. We train our network using a dataset from four metropolitan areas in the U.S. on two different zoom levels. Our results show that adding aleatoric uncertainty can improve the IoU scores if a sufficient amount of informative data is provided