Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage

Options and recommandations related to further development of an Espon Cartographic Language

In this 5th part of Espon Cartographic Language Final Report, our aim is to identify good practices, as well in the development of interactive cartographic environments such as atlases, as in innovative cartographic constructions. Our proposals target several levels:- The level of applications themselves: which functionalities have to be use, for what applications and what objectives?-The level of cartographic representations, meaning the possibilities to introduce elements of animation and interactivity in maps, depending on data and objectives: what innovations for which representation?To achieve such aims, we use two types of resources:- a collection of interactive atlases, considered as the most representative of the diversity in european statistical atlases, which we have analyzed and compared.- the collection of maps presented in Task 4, that we propose to enrich with functions of interaction and animation.The first part of Task 5 deals with recommendations, coming from a comparative analysis of european statistical atlases. These recommendations depend on the type of environment to be made (environment of visualization, analysis or exploration), and on the desired interactivity level.The second part deals with recommendations to create interactive and animated maps. They are illustrated by concrete proposals, in the form of summary datasheet.The final part deals with a comparison of computer tools that can be used to make innovative cartographic applications

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseasesThis work was partially funded by The Spanish Ministry of Economy and Competitiveness with European Regional Development Fund [grant numbers PID2019-108096RB-C21 and PID2019-108096RB-C22]; the European Food Safety Authority [grant number GP/EFSA/ENCO/2020/02]; the Andalusian Government with European Regional Development Fund [grant numbers UMA18- FEDERJA-102 and PAIDI 2020:PY20-00372]; Fundacion Progreso y Salud [grant number PI-0075-2017], also from the Andalusian Government; the Ramón Areces foundation, which funds project for the investigation of rare disease (National call for research on life and material sciences, XIX edition); the University of Malaga (Ayudas del I Plan Propio) and the Institute of Health Carlos III which funds the IMPaCT-Data project. The CIBERER is an initiative from the Institute of Health Carlos III. The conclusions, findings and opinions expressed in this scientific paper reflect only the view of the authors and not the official position of the European Food Safety Authority. Partial funding for open access charge: Universidad de Málag

Enabling European archaeological research: The ARIADNE E-infrastructure

Research e-infrastructures, digital archives and data services have become important pillars of scientific enterprise that in recent decades has become ever more collaborative, distributed and data-intensive. The archaeological research community has been an early adopter of digital tools for data acquisition, organisation, analysis and presentation of research results of individual projects. However, the provision of einfrastructure and services for data sharing, discovery, access and re-use has lagged behind. This situation is being addressed by ARIADNE: the Advanced Research Infrastructure for Archaeological Dataset Networking in Europe. This EUfunded network has developed an einfrastructure that enables data providers to register and provide access to their resources (datasets, collections) through the ARIADNE data portal, facilitating discovery, access and other services across the integrated resources. This article describes the current landscape of data repositories and services for archaeologists in Europe, and the issues that make interoperability between them difficult to realise. The results of the ARIADNE surveys on users' expectations and requirements are also presented. The main section of the article describes the architecture of the einfrastructure, core services (data registration, discovery and access) and various other extant or experimental services. The ongoing evaluation of the data integration and services is also discussed. Finally, the article summarises lessons learned, and outlines the prospects for the wider engagement of the archaeological research community in sharing data through ARIADNE