A containment-first search algorithm for higher-order analysis of urban topology

Research has revealed the importance of the concepts from the mathematical areas of both topology and graph theory for interpreting the spatial arrangement of spatial entities. Graph theory in particular has been used in different applications of a wide range of fields for that purpose, however not many graph-theoretic approaches to analyse entities within the urban environment are available in the literature. Some examples should be mentioned though such as, Bafna (2003), Barr and Barnsley (2004), Bunn et al. (2000), Krüger (1999), Nardinochi et al. (2003), and Steel et al. (2003). Very little work has been devoted in particular to the interpretation of initially unstructured geospatial datasets. In most of the applications developed up-to-date for the interpretation and analysis of spatial phenomena within the urban context, the starting point is to some extent a meaningful dataset in terms of the urban scene. Starting at a level further back, before meaningful data are obtained, the interpretation and analysis of spatial phenomena are more challenging tasks and require further investigation. The aim of retrieving structured information from initial unstructured spatial data, translated into more meaningful homogeneous regions, can be achieved by identifying meaningful structures within the initial random collection of objects and by understanding their spatial arrangement (Anders et al., 1999). It is believed that the task of understanding topological relationships between objects can be accomplished by both applying graph theory and carrying out graph analysis (de Almeida et al., 2007)

A Graph-based Approach for Higher Order Gis Topological Analysis

Retrieving structured information from an initial random collection of objects may be carried out by understanding the spatial arrangement between them, assuming no prior knowledge about those objects. As far as topology is concerned, contemporary desktop GIS packages do not generally support further analysis beyond adjacency. Thus, one of the original motivations of this work was to develop new ideas for scene analysis by building up a graph-based technique for better interpretation and understanding of spatial relationships between GIS vector-based objects beyond its first level of adjacency; the final aim is the performance of some kind of local feature organization into a more meaningful global scene by using graph theory. As the example scenario, a LiDAR data set is being used to test the technique that we plan to develop and implement. After the generation of the respective TIN, two different binary classifications were applied to the TIN facets (based on two different slope thresholds) and TIN facets have been aggregated into homogeneous polygons according to their slope characteristics. A graph-based clustering procedure inside these polygonal regions, by establishing a neighbourhood graph, followed by the delineation of cluster shapes and the derivation of cluster characteristics in order to obtain higher level geographic entities information (regarding sets of buildings, vegetation areas, and say, land-use parcels) is object of further work. The results we are expecting to obtain might be useful to support land-use mapping, image understanding or, generally speaking, to support clustering analysis and generalization processes

Graph theory in higher order topological analysis of urban scenes

Interpretation and analysis of spatial phenomena is a highly time-consuming and laborious task in several fields of the Geomatics world. That is why the automation of these tasks is especially needed in areas such as GISc. Carrying out those tasks in the context of an urban scene is particularly challenging given the complex spatial pattern of its elements. The aim of retrieving structured information from an initial unstructured data set translated into more meaningful homogeneous regions can be achieved by identifying meaningful structures within the initial collection of objects, and by understanding their topological relationships and spatial arrangement. This task is being accomplished by applying graph theory and by performing urban scene topology analysis. For this purpose, a graph-based system is being developed, and LiDAR data are currently being used as an example scenario. A particular emphasis is being given to the visualisation aspects of graph analysis, as visual inspections can often reveal patterns not discernable by current automated analysis techniques. This paper focuses primarily on the role of graph theory in the design of such a tool for the analysis of urban scene topology.http://www.sciencedirect.com/science/article/B6V9K-4P6MPBP-2/1/e1b4066db2881db3de31085d779a27c

Potent Trivalent Inhibitors of Thrombin through Hybridization of Salivary Sulfopeptides from Hematophagous Arthropods

Blood feeding arthropods, such as leeches, ticks, flies and mosquitoes, provide a privileged source of peptidic anticoagulant molecules. These primarily operate through inhibition of the central coagulation protease thrombin by binding to the active site and either exosite I or exosite II. Herein, we describe the rational design of a novel class of trivalent thrombin inhibitors that simultaneously block both exosites as well as the active site. These engineered hybrids were synthesized using tandem diselenide-selenoester ligation (DSL) and native chemical ligation (NCL) reactions in one-pot. The most potent trivalent inhibitors possessed femtomolar inhibition constants against alpha-thrombin and were selective over related coagulation proteases. A lead hybrid inhibitor possessed potent anticoagulant activity, blockade of both thrombin generation and platelet aggregation in vitro and efficacy in a murine thrombosis model at 1 mg kg(-1). The rational engineering approach described here lays the foundation for the development of potent and selective inhibitors for a range of other enzymatic targets that possess multiple sites for the disruption of protein-protein interactions, in addition to an active site

VGI quality control

This paper presents a framework for considering quality control of volunteered geographic information (VGI). Different issues need to be considered during the conception, acquisition and post-acquisition phases of VGI creation. This includes items such as collecting metadata on the volunteer, providing suitable training, giving corrective feedback during the mapping process and use of control data, among others. Two examples of VGI data collection are then considered with respect to this quality control framework, i.e. VGI data collection by National Mapping Agencies and by the most recent Geo-Wiki tool, a game called Cropland Capture. Although good practices are beginning to emerge, there is still the need for the development and sharing of best practice, especially if VGI is to be integrated with authoritative map products or used for calibration and/or validation of land cover in the future

THE REVIEWING PROCESS FOR ISPRS EVENTS

Following the first initiatives taken by the International Programme Committee of the XXIIIrd ISPRS Congress in Prague (Czech Republic) in 2016, modifications of the reviewing process of ISPRS events were further considered during the years 2017 and 2018. This evolution first targets to better fit such a process to the currents requirements and expectations of the ISPRS community. Secondly, it aims to provide unified guidelines for the different steps of the process. Under the aegis of the 2020 Congress Director and ISAC (International Science Advisory Committee) chair, several discussions were held in-between September 2017 and June 2018 with ISAC members, Technical Commission Presidents (TCP), council members, 2016 and 2020 Congress Programme Chairs. This document serves as a unique transparent basis that applies for all kinds of ISPRS events (from Congress and Geospatial Week to smaller workshops), and all categories of people that are bound to be involved in the evaluation process of scientific contributions (authors, reviewers, TCPs, … ). It also specifies the evaluation criteria for the works submitted to ISPRS events, both for full papers and abstracts. Subsequently, it helps authors to improve the content and shape of their contributions. Eventually, this paper is targeted to help new chairs to smoothly prepare their future event. The following guidelines were first adopted for the 2018 Technical Commission Symposia

Joint 3D estimation of vehicles and scene flow

driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method. © 2015 Copernicus GmbH. All Rights Reserved

An iterative inference procedure applying conditional random fields for simultaneous classification of land cover and land use

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result

Gaussian process for activity modeling and anomaly detection

Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set

Global and local sparse subspace optimization for motion segmentation

In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time