TEAM 11: Expanding Open Land Use Map by Terrain Characteristics

TEAM LEADER: Karel Jedlička

TEAM MEMBERS: Marcela Doubková,  Dmitrij Kožuch

PROJECT IDEA: The idea is to expand Open Land Use map by computing main terrain characteristics of agricultural fields (LPIS blocks). So far for computation two datasets will be used 1arcsecond DEM dataset by USGS and Open Land Use map (for masking fields). The experimental area will be Weinviertel (province located in the norteast of Lower Austra).

So far it is possible to get the main terrain characteristics of the field by entering its unique id in Open Land Use dataset. For example here are those characteristics for the field with id 10145238 .

{‘min_elevation’: 186.64203, ‘max_elevation’: 196.92177, ‘mean_elevation’: 190.70232, ‘median_elevation’: 190.44339, ‘min_slope’: 0.7573741, ‘max_slope’: 1.5695069, ‘mean_slope’: 1.2346609, ‘median_slope’: 1.2555954, ‘min_azimuth’: -179.98296, ‘max_azimuth’: 179.62314, ‘mean_azimuth’: -13.073845, ‘median_azimuth’: -30.859669}

Otherwise as well as get the statistics it is also possible to download characteristics as TIF images:

INSPIRE Hackathon 2018 – Special Prize on Security

The INSPIRE Hackathon organisers will award a special prize in the value of 1000 EUR to a team that will address in the best possible way the issue of security, considering aspects related to the security of citizens and countries as the ones addressed in the framework of the NextGEOSS and EVER-EST projects. In the scope of this prize, security doesn’t include security of data and information from the ICT point of view.

More information about the hackathon can be found here.

TEAM 10: Location Intelligence from Multi-Variate Spatial Analysis

TEAM LEADERS: Runar Bergheim, Karel Charvat

TEAM MEMBERS: Petr Uhlir, Raitis Berzins, Dmitrij Kozuk, Milan Kalas

PROJECT IDEA:

A lot of energy has gone into the development of precision data both with regards to fundamental geospatial data such as basemaps and thematic data serving a single purposes for specific and narrow target audiences.  This idea seeks to use such data to elaborate detailed characteristics about places based on the co-occurrence of certain features or phenomena.

 

An example of how such characteristics could be used, let us consider the following. The accessibility of a place may be described in terms of its proximity to transport hubs for air, train and road transport. That gives a snapshot of the current state of the area; however — by incorporating planned and future developments, it is possible to characterize a place by how it is likely to be two years from now. The climate of a place can be described in terms of monthly averages, averaged over 50 years of aggregated data for precipitation, air temperature, sea temperature, cloud cover, snow cover etc. The terrain can be characterized in terms of its ruggedness, whether it is a platou, a plane, coastal, mountaineous or otherwise. There is a near infinite number of characteristics that can be considered — in themselves they are not necessarily particularly useful — but combined the right way they may predict trends and offer location insights that are useful both to individuals, private enterprises and regional development bodies.

 

I.e. by identifying all places in the mountains that has rugged terrain and that has a long and steady period of snow cover with cold frequent sunny days — and with a new infrastructure hubs being developed within 75 minutes drive away — but scores low on availability of visitor oriented services we have established a dormant economic potential. This sort of location intelligence is thus far the material of reports.

 

This team will be operating in a mixed technical and non-technical manner. On the one hand we will expand upon the business cases in an exploratory manner through conversation; on the other we will try to identify practical sources and algorithms to determine key characteristics for places, taking as a starting point a seed database of about 20 000 locations and a bunch of climate, land-cover and landscape characteristics that have already been calculated.

TEAM 9: SeWa – Sentinel Watcher

TEAM LEADER: Marek Šplíchal (Lesprojekt)

TEAM MEMBERS: Jan Sháněl (MUNI)

PROJECT IDEA: Map based web application for identifying of usable remote sensing data from Sentinel satellites. A user can choose one or multiple positions (for example fields, forests etc.) and the application prepare a forecast based on location, minimal satellite elevation and minimal crossing duration for Sentinel 2A and 2B satellites. The application calculates timetable of satellites crossing time and weather (clouds) forecast as a result. User gets information when his selected position(s) can be photographed and which imagery can be used for further processing.

The DataBio Project Starts Trials of 26 Bioeconomy Pilots

7th August 2018, DataBio Press Release:

The development of a sustainable bioeconomy in Europe is expected to get a big boost from Big Data technologies thanks to a project co-funded by the EU’s Horizon 2020 programme.

The European Union’s Data-Driven Bioeconomy (DataBio) project has announced the launch of 26 different pilot trials, which are already underway in 17 countries. They will provide “real world” insights relevant for policymakers and producers – the farmers, foresters and fishermen engaged in Europe’s bioeconomy.

DataBio is using Big Data technologies to support the growth of Europe’s bioeconomy. More specifically, the project is handling massive flows of data collected through sensors placed in the soil and air, as well as from aerial and satellite imagery.

The DataBio consortium includes 48 partners from 17 countries and over 100 associated organisations.

The project’s mission is driven by the development, use and evaluation of the 26 new pilots covering agriculture (13), forestry (7) and fishery (6). The aim is to contribute to the production of the best possible raw materials from the three sectors to improve the output of food, energy and biomaterials.

The project is deploying over 90 state-of-the-art Big Data, Earth Observation and ICT technologies, linked together through the DataBio Platform. DataBio modelled the pilots and the technologies from a number of perspectives (e.g. technical and data, business motivation and processes, strategic) and developed the first version of the DataBio platform. The technologies have been matched and combined with each other to form innovative complex solutions for each pilot.

Now that the pilots have started their first trials, the project is one step closer to achieving its goal to demonstrate how these solutions can offer real added value to bioeconomy businesses.

The DataBio project coordinator, from INTRASOFT International, Dr Athanasios Poulakidas, said: “We are very excited, anticipating tangible success stories showing there is real value for all in using Big Data technologies in bioeconomy.”

Agriculture pilots

Precision agriculture in olives, fruits, grapes (Greece): A smart farming pilot to promote sustainable practices by providing policy advice on irrigation, fertilisation and pest/disease management. The exploitation of heterogeneous data, facts and scientific knowledge is aimed to facilitate decision-making and ensure smooth implementation of policy advice in the field. Deployed at three different sites in Greece, the pilots target olives, peaches and grapes.

Precision agriculture in vegetable seed crops (Italy): Harvesting plants at the right stage of maturity is vital to ensure the seed produced is of high quality. Currently, it is up to farmers, with the help of seed experts, to decide about harvesting and this is usually based on experience and observation. The scope of the pilot is to support farmers with the use of satellite telemetry.

Precision agriculture in vegetables seed crops (Netherlands): Potato growers aim to furnish them with higher and more predictable yields in a sustainable manner. Farmers will use a crop monitoring and benchmarking system using satellite data that provides information on the crop status based on weather data and greenness index data.

Big Data management in greenhouse eco-systems (Italy): This pilot implements genomic selection models, with particular focus on tomatoes, to support the greenhouse horticulture value chain.

Cereals, biomass and fibre crops (Spain): Using Earth Observation imageries and Internet of Things (IoT) sensor data, the pilot will map different areas in Spain and set up an informative management system for irrigation and early warning of heterogeneities or malfunctions of irrigation systems. The users of this service will be farmers, irrigation communities and public administrations.

Cereals, biomass and fibre crops (Greece): A smart farming pilot to promote sustainable practices by providing policy advice on irrigation. The exploitation of heterogeneous data, facts and scientific knowledge is aimed to facilitate decision-making and ensure smooth implementation of policy advice in the field. The target crop type is cotton.

Cereals, biomass and fibre crops (Italy): The pilot uses remote and proximal sensors for biomass crop prediction and management. The biomass crops include sorghum, fiber hemp and cardoon that can be used for several purposes including biofuel, fiber, and biochemicals respectively.

Cereals, biomass and fibre crops (Czech Republic):  To develop the web-based webGIS platform for mapping crop vigour, this pilot integrates Earth Observation data as a support tool for variable rate application of fertilisers and crop protection. This includes identification of crop status, mapping of spatial variability and delineation of management zones.

Machinery management (Czech Republic): This pilot is focused mainly on collecting telematic data from tractors and other farm machinery to analyse and compare to other farm data. The main goal is to collect and integrate data and receive comparable results. A challenge associated with this pilot is that a farm may have tractors and other machinery from manufacturers that use different telematic solutions and data ownership/sharing policies.

Insurance (Greece): To promote a damage assessment methodology and services dedicated to the agricultural insurance market, this pilot will eliminate the need for on-the-spot checks and to speed up the claims pay-out process. It uses data from Earth Observation platforms and Internet of Things agro-climate sensors to assess the impact of climate-related systemic perils (e.g. high/low temperatures, flood, drought) on high-value crops.

Farm Weather Insurance Assessment (Italy): The aim of this pilot is to provide and assess a test area of services for the agriculture insurance market, in particular risk assessment related to weather conditions and damage assessment. It is based on the analysis of satellite data, which is correlated with meteorological data and other ground-available data.

Common Agricultural Policy (CAP) Support (Italy and Romania): The objective of this pilot is to support the CAP by utilising Earth Observation data to identify the crop types in farm areas. Products and services will be fine-tuned to achieve requirements set out in the 2015-2020 EU CAP policy. The pilot will provide information layers and indicators to support European Paying Agencies with different levels of aggregation and details up to farm level.

Common Agricultural Policy (CAP) Support (Greece): This pilot evaluates a set of Earth Observation-based crop classification services, which deal effectively with the newly introduced CAP demands for systematic multi-crop agricultural monitoring, tracking and assessment of eligibility conditions. The proposed services use “traffic lights” colour-coding to protect the farmers against errors during the submission of greening applications.

Forestry pilots

Easy data sharing and networking (Finland): The Wuudis platform is used to interface with Finland’s forest authority. New networking features have been added, such as a work quality monitoring app. This will streamline and ensure transparency of subsidy payments, saving enormous amounts of time.

Monitoring and control tools for forest owners (Finland): This pilot adds crowdsourcing services to the Wuudis platform, interfacing with Finland’s forest service portal. This aims to better monitor forest damage (such as storms, snow, pests, diseases).

Forest damage remote sensing (Finland): This pilot aims to create a forest inventory monitoring service by using data collected by Unmanned Aerial Vehicles (UAVs) on Wuudis.

Monitoring of forest health (Spain): Using satellite data, Unmanned Aerial Vehicles (UAV) imagery and field data, this pilot is developing a methodology for the early detection and monitoring of plagues and diseases affecting forests. The study cases are Gonipterus affecting Eucalyptus and Phytophtora affecting Quercys Illex. The final product will be used by public administrations for optimal decision-making.

Invasive alien species control and monitoring (Spain): Using Big Data sources such as trading and travelling datasets, in addition to weather and climate information, this pilot is developing a methodology for the creation of Alien Invasive Species risk maps. The indexes obtained are related to the susceptibility of ecosystems to be invaded. The final product will be used by public administrations.

Web-mapping service for government decision-making (Czech Republic): Big satellite data are processed in novel ways to yield forest health trends. To do so, all available satellite observations are used, validated against extensive in-situ database of forest health. Resulting forest health maps are published as a web-mapping service and used by the Ministry of Agriculture for subsidy payments.

Shared multiuser forest data environment (Finland): Finland’s forestry service portal will be extended to offer more forest data services, including using crowdsourced ones and connectivity with the national service architecture. It will encourage the utilisation of standardised and open forest data for developing new services that will benefit the entire forestry sector.

Fishery pilots

Oceanic tuna fisheries immediate operational choices: The goal of this pilot is to improve vessel energy efficiency in oceanic tuna fisheries and engine preventive maintenance by providing support tools for operational choices, such as vessel loading, weather routing and condition-based maintenance, for Basque fishery vessels. Big Data approaches will build models using engine, propulsion, meteorological and historical data and the vessels design.

Small pelagic fisheries immediate operational choices: This pilot focuses on energy optimisation in Norwegian pelagic fisheries through operational support tools for deciding propulsion mode (diesel-electric, diesel-mechanic and various hybrid configurations) and power generation (use of shaft generator and auxiliary engines). The expected outcome is an on-board application that displays in real-time, current fuel consumption and the best alternatives for propulsion modes and energy production.

Oceanic tuna fisheries planning: The aim of this pilot is to use data from Earth Observation satellites and from buoys used by fishermen, to produce real-time fisheries route planning. This will also allow for eco-friendlier fisheries by means of consuming less fuel for fishing in a sustainable manner.

Small pelagic fisheries planning: This pilot aims to demonstrate how the use of Big Data technologies can provide crew and shipowners with information in a manner that benefits fisheries planning, for example for finding the best suitable fishing grounds. This pilot targets Norway’s small pelagic fisheries.

Pelagic fish stock assessments: The pilot aims to show that a combination of information from various data assets may help produce better fish population estimates for pelagic fish stocks in the Norwegian fishing zones. It is anticipated that a crowdsourced data collection effort combined with public/private data assets and data analytics can increase both the accuracy and precision of stock assessments.

Small pelagic market predictions and traceability: This pilot will leverage Big Data technologies for pelagic price prediction based on various market data relevant for pelagic fisheries, such as catch and landing data and International fisheries export and market data. Near-future pelagic price predictions will be used for catch value optimisation.

 

For further information, check https://www.databio.eu/en/

Contact us: info@databio.eu

TEAM 8: SPOI Data Enrichment

TEAM LEADER: Raúl Palma (PSNC),

TEAM MEMBERS: Otakar Čerba (UWB), Stein Runar Bergheim (AVINET),  Raitis Berzins (BOSC), Milan Kalas (KAJO)

PROJECT IDEA:  Smart Points of Interest (SPOI) increased the number of points from about 27M to more than 30M. The goal of this working group is to improve the SPOI dataset, both by extending and improving the underlying model (ontology), and to enrich the knowledge base with links to other relevant datasets.  Regarding the ontology, we aim to include properties definitions, mappings to other vocabularies to the current taxonomy of classes, and possibly additional terms for annotations/ratings,

Regarding the dataset, we aim to discuss and find possibility of linking with review/ratings datasets, relevant eurostat indicators, and others that may be identified during the hackathon.

TEAM 7: LPIS RDF Integration

TEAM LEADER: Raul (PSNC)

TEAM MEMBERS: Sam (PSNC), Vojta (Lesprojekt)

PROJECT IDEA: The idea is to integrate Czech LPIs data with other RDF datasets, including the definition of sample sparql queries based on relevant use cases. We will use FOODIE ontology. FOODIE ontology has been generated from FOODIE application schema (UML model), Revision 4.3.2, and translated into an ontology according to ISO/DIS 19150-2 with several modification using ShapeChange.

For the beginning we defined next use cases:

Use Case #1 – buffer zones around water bodies (user will specify the distance). Result is a new shp datasets which define the areas within the fields with limited/restricted application of agro-chemicals.

Input: water bodies + LPIS

Use Case #2 – select of Farm based on the ID_UZ attribute from public LPIS database and searching EO data over all fields

Use Case #3 – visualization of crop species based on the farm data (need parcels with crop types- not available from open LPIS data) + percentage of crops in graphs

Use Case #4 – select fields with different soil  types

Use Case #5 something more complex – to select all fields with certain crop in max distance from certain point (it could be for logistic, distribution of biomass etc)

TEAM 6: Sensor Data Streamed as RDF on the Fly

TEAM LEADER: Raul (PSNC)

TEAM MEMBERS: Sam (PSNC), Mike, Ondra (UWB)

PROJECT IDEA: SensLog is web-based sensor data management system. SensLog is a solution that is suitable for static in-situ monitoring devices as well as for mobile devices with live tracking ability.SensLog provides system of web-services with JSON format encoding or provides standardized services using core methods of OGC SOS version 1.0.0. The latest version of REST API is following CRUD schema.

The idea is to generate RDF data from SensLog on the fly, so that it can be streamed all the observations in real time.