TEAM 6: Integration of Meteoblue Data with Sensors and Visualisation Client

This is the team no. 5 for the Patras DataBio Hackathon 2019.

Team mentors: Marek Splichal, Raitis Berzins, Michal Kepka

Description: For the purposes of this hackathon, Meteoblue data / image / map API for weather forecasts and historic data will be available. The goal of this team is to support integration of these data with HSLayers NG and SensLog and provide climatic services.

TEAM 5: Unusual Data Exploration

This is the team no. 5 for the Patras DataBio Hackathon 2019.

Team mentors: Pavel Hájek, Runar Bergheim, Raul Palma

Description: We would like to inspect opportunities of statistical data across the Europe, in order to explore new, unexpected patterns in data. First of all, any relevant statistical data about demography, agriculture, industry, etc. need to be found and inspected. Then, such data should be consolidated to statistical areas of NUTS3 regions, if they are not already. And finally, appropriate (multi criterion) analysis should be applied that may  unveil unusual data patterns and relations.

TEAM 4: Sharing Maps as Intelligent Objects

This is the team no. 4 for the Patras DataBio Hackathon 2019.

Team mentors: Jan Vrobel, Simon Leitgeb, Raitis Berzins

Description: Today we propose to you the idea that “maps” are interesting not only as visualizations of Agriculture  data capturing — but as shareable, fascinating and valuable Agriculture objects in themselves. Once, a map used to be an expensive rolled up scroll of calves skin that was drawn by a skilled artist from the manuscripts of daring sea-farers in the great age of discovery. Later, maps were produced by less picturesque but more efficient means – until the advent of the GIS age when a lot of people who previously couldn’t suddenly could make professional LOOKING maps. Nowadays, a map is not a “flat image” but a complex layered object that references data sources ‘scattered’ across a decentralized, democratic and at times volatile Internet.

Our needs are many — and very different — but so are our skill sets; thus offering everyone sophisticated GIS tools capable of making their own maps is not a likely path to ‘happy forever after. It is often simpler, better and more effective to simply give them a “map”.

There currently exists hundreds of services offering spatial information through real-time interactive protocols such as WMS and WFS etc. Soon, if member states and signatories to INSPIRE do as they are legally obliged, this number will be thousands — ten thousands.

The fact that a map is a composite object referring to a lot of live data sources around the net, require the existence of a “Map Composition” standard that describes the elements that constitute a map and how they should be combined to fit together neatly.

An early effort by the OGC was the Web Map Context specification that has not evolved since 2005. This little bit ‘heavyweight’ XML-based standard is limited in scope and has not evolved with the developments in standards and technology in the  years that have passed since its creation. Recently the three European Community funded projects SDI4Apps, Foodie and OTN have started the work of defining a simple, lightweight specification for Map Compositions using HTML5- and bandwidth friendly JavaScript Object Notation (JSON) as a carrier of information.

The current specification of the JSON Map Composition is available on the GitHub Wiki of HSLayers NG.

Initially Concept of Map Composition was supported by HSlayers NG. During Prague INSPIRE Hackathon were implemented pilot  Environmental Atlas of the Liberec Region in QGIS, where for QGIS was developed plugin, which support access to map compositions from server. This development now continue and QGIS is now also able to publish Maps on Web.

We would like to test this approach in different cases during this hackathon. The goal is to connect desktop and web GIS.

Team 3: Sensor Data Visualisation

This is the team no. 3 for the Patras DataBio Hackathon 2019.

Team mentors: Michal Kepka, Raitis Berzins

Description: The aim of this team is to test visualisation frameworks for sensor data. Sensor data are important part of spatial data. Effective and interactive visualisation of such data in the form of charts, maps and tables brings more information to users. There are plenty of chart libraries for sensor data in different level of interactivity and development freedom. During this hackathon we would like to test a visualisation grammar – Vega, that is declarative language for creating, saving, and sharing interactive visualisation designs. With connection to SensLog we will test visualisation of sensor data produced by different types of agrometeo sensors.

TEAM 2: Agriculture Plastics in the fields of Finland

This is the team no. 2 for the Patras DataBio Hackathon 2019.

Team mentors: Jiri Kvapil, Karel Charvat, Tuula Löytty

Project description:

  1. Exploit Earth Observation data to gain accurate and up-to-date information of number of bales in the fields  The reference data is here (link).  
  1. Demonstrate  how to visualize the bale film usage on the fields in Päijät-Häme, Finland. Each bale takes about 70 m of film.The ‘standard” bale is  750mm x 1500mm rolls but there is variation. The thickness of the plastic film is  25‑30 µm. There are 4-8 layers. Film weight/bale varies from 500-1700 g. The output can be used for example to estimate the market size of bale wrapping films in a region.
  1. The third task is to map the different kinds of agriculture plastic films such as the covering films and nets for greenhouse, low or medium tunnel, soil mulching and solarisation films, irrigation and drainage pipes.

The background:

More than 80% of plastics found in marine environments has been produced, consumed and disposed of on land. In addition to inadequate end-of-life treatment of plastic waste, plastics reaches our soils through increasing use for agricultural purposes. According to APE-Europe about 750 000 tn of agriculture plastics and films is delivered to the farms annually. The image on the right performs the use in European countries annually.

The treatment of the Agriculture Plastic Waste (APW) has two main phases:  1) the plastic waste collection from the farms and 2) recycle to granules. In most European countries the collection rate is about 20-50 %. In Finland it’s 20%.  Due to the lack of coverage and functioning collecting system, the farmers dump APW to landfill, burn it at farm and cause emissions, bury it on soil or just stock the plastics on farm. The farmers want to act in a responsible manner, but the missing part is the collecting system. By continuing this manner, we waste valuable raw-material and cause damage on soil, air and seas.  The goal is to collect 100 % of the APW by 2030.

In order to reserve adequate, but not excess, facilities, machinery and man resources for collecting APW,  it’s necessary to have accurate and up-to-date information of the used agriculture plastics on the farms. The agriculture plastics distributors have some information, but there are gaps and it can lead to wrong assumptions.

TEAM 1: Management of the HSLayers Architecture

This is a short desctription of the team no 1 of the Patras DataBio Hackathon 2019 that aims to define an architecture of the software for spatial data visualisation of HSLayers – with consideration of agriculture applications.

Team members: Šimon Leitgeb, Raitis Berzins, František Zadražil, Filip Leitner, Marek Šplíchal

Project description: Discuss and agree on the culture of HSLayers development. Refactoring of HSLayers and development of specific reusable components like:

  • Loading vector layers to map
  • Conversion of SHP file to GeoJSON
  • Displaying of attribute table with the ability to edit