Local dynamics for fibered holomorphic transformations

Fibered holomorphic dynamics are skew-product transformations over an irrational rotation, whose fibers are holomorphic functions. In this paper we study such a dynamics on a neighborhood of an invariant curve. We obtain some results analogous to the results in the non fibered case

Spatial information retrieval and geographical ontologies: an overview of the SPIRIT project

A large proportion of the resources available on the world-wide web refer to information that may be regarded as geographically located. Thus most activities and enterprises take place in one or more places on the Earth's surface and there is a wealth of survey data, images, maps and reports that relate to specific places or regions. Despite the prevalence of geographical context, existing web search facilities are poorly adapted to help people find information that relates to a particular location. When the name of a place is typed into a typical search engine, web pages that include that name in their text will be retrieved, but it is likely that many resources that are also associated with the place may not be retrieved. Thus resources relating to places that are inside the specified place may not be found, nor may be places that are nearby or that are equivalent but referred to by another name. Specification of geographical context frequently requires the use of spatial relationships concerning distance or containment for example, yet such terminology cannot be understood by existing search engines. Here we provide a brief survey of existing facilities for geographical information retrieval on the web, before describing a set of tools and techniques that are being developed in the project SPIRIT : Spatially-Aware Information Retrieval on the Internet (funded by European Commission Framework V Project IST-2001-35047)

QuantiFERON®-TB gold in-tube performance for diagnosing active tuberculosis in children and adults in a high burden setting.

To determine whether QuantiFERON®-TB Gold In-Tube (QFT) can contribute to the diagnosis of active tuberculosis (TB) in children in a high-burden setting and to assess the performance of QFT and tuberculin skin test (TST) in a prospective cohort of TB suspect children compared to adults with confirmed TB in Tanzania. Sensitivity and specificity of QFT and TST for diagnosing active TB as well as indeterminate QFT rates and IFN-γ levels were assessed in 211 TB suspect children in a Tanzanian district hospital and contrasted in 90 adults with confirmed pulmonary TB. Sensitivity of QFT and TST in children with confirmed TB was 19% (5/27) and 6% (2/31) respectively. In adults sensitivity of QFT and TST was 84% (73/87) and 85% (63/74). The QFT indeterminate rate in children and adults was 27% and 3%. Median levels of IFN-γ were lower in children than adults, particularly children <2 years and HIV infected. An indeterminate result was associated with age <2 years but not malnutrition or HIV status. Overall childhood mortality was 19% and associated with an indeterminate QFT result at baseline. QFT and TST showed poor performance and a surprisingly low sensitivity in children. In contrast the performance in Tanzanian adults was good and comparable to performance in high-income countries. Indeterminate results in children were associated with young age and increased mortality. Neither test can be recommended for diagnosing active TB in children with immature or impaired immunity in a high-burden setting

Comap: A synthetic dataset for collective multi-agent perception of autonomous driving

Collective perception of connected vehicles can sufficiently increase the safety and reliability of autonomous driving by sharing perception information. However, collecting real experimental data for such scenarios is extremely expensive. Therefore, we built a computational efficient co-simulation synthetic data generator through CARLA and SUMO simulators. The simulated data contain image and point cloud data as well as ground truth for object detection and semantic segmentation tasks. To verify the superior performance gain of collective perception over single-vehicle perception, we conducted experiments of vehicle detection, which is one of the most important perception tasks for autonomous driving, on this data set. A 3D object detector and a Bird's Eye View (BEV) detector are trained and then test with different configurations of the number of cooperative vehicles and vehicle communication ranges. The experiment results showed that collective perception can not only dramatically increase the overall mean detection accuracy but also the localization accuracy of detected bounding boxes. Besides, a vehicle detection comparison experiment showed that the detection performance drop caused by sensor observation noise can be canceled out by redundant information collected by multiple vehicles

Joint estimation of road roughness from crowd-sourced bicycle acceleration measurements

In contrast to cars, route choices for cycling are barely influenced by the respective traffic situation, but to a large extent by the routes' comfort. Especially in urban settings with several alternatives, segments with many or long stops at traffic lights and badly maintained roads are avoided due to a low comfort and cyclists vary from the shortest route. This fact is only indirectly considered in common navigation applications. This work aims to integrate surface roughness measurements collected from diverse bicycles to a joint scale via a least-squares adjustment. Data was collected using smartphones, which were mounted to bike hand bars and measured positions and vertical accelerations on user's trips. As this way sensed roughness also depends on the bike setting and type, the resulting values would be different for different users. Thus, this paper presents a novel approach to harmonize observations from differing sensitive setups. The basic concept idea of bundle block adjustment is adapted to calibrate a basic scale model and in parallel adjust the observations of surface roughness to a common scale. This way a crowd-sourced roughness map can be generated. Such a map can be used to enrich bike focused routing services and thus encourage cycling in daily live. In addition, it can also be used to derive hints for infrastructure servicing

Building Change Detection in Airborne Laser Scanning and Dense Image Matching Point Clouds Using a Residual Neural Network

National Mapping Agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) sensors as well as using Dense Image Matching (DIM) on aerial images. As these datasets are often captured years apart, they contain implicit information about changes in the real world. While detecting changes within point clouds is not a new topic per se, detecting changes in point clouds from different sensors, which consequently have different point densities, point distributions and characteristics, is still an on-going problem. As such, we approach this task using a residual neural network, which detects building changes using height and class information on a raster level. In the experiments, we show that this approach is capable of detecting building changes automatically and reliably independent of the given point clouds and for various building sizes achieving mean F1-Scores of 80.5% and 79.8% for ALS-ALS and ALS-DIM point clouds on an object-level and F1-Scores of 91.1% and 86.3% on a raster-level, respectively

Joint classification of ALS and DIM point clouds

National mapping agencies (NMAs) have to acquire nation-wide Digital Terrain Models on a regular basis as part of their obligations to provide up-to-date data. Point clouds from Airborne Laser Scanning (ALS) are an important data source for this task; recently, NMAs also started deriving Dense Image Matching (DIM) point clouds from aerial images. As a result, NMAs have both point cloud data sources available, which they can exploit for their purposes. In this study, we investigate the potential of transfer learning from ALS to DIM data, so the time consuming step of data labelling can be reduced. Due to their specific individual measurement techniques, both point clouds have various distinct properties such as RGB or intensity values, which are often exploited for classification of either ALS or DIM point clouds. However, those features also hinder transfer learning between these two point cloud types, since they do not exist in the other point cloud type. As the mere 3D point is available in both point cloud types, we focus on transfer learning from an ALS to a DIM point cloud using exclusively the point coordinates. We are tackling the issue of different point densities by rasterizing the point cloud into a 2D grid and take important height features as input for classification. We train an encoder-decoder convolutional neural network with labelled ALS data as a baseline and then fine-tune this baseline with an increasing amount of labelled DIM data. We also train the same network exclusively on all available DIM data as reference to compare our results. We show that only 10% of labelled DIM data increase the classification results notably, which is especially relevant for practical applications

Incremental map refinement of building information using lidar point clouds

For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty - even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System

Exploring ALS and DIM data for semantic segmentation using CNNs

Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96% in an ALS and 83% in a DIM test set