MENTOR: Jaroslav Šmejkal

The call will cover small food producers operating in a specific region. They also support employment in the region and also promote their uniqueness, for example by selling their specific food products. Sales can be made to locals, tourists, or even in remote regions. For this type of supply chain, it has proved crucial that local food producers are often unable to ensure the consistent quality of food transport conditions from production to the retailer. Local food producers are mostly small producers. Therefore, they do not rely on professional technologies to transport food. The challenge will be to monitor the quality of their products during transport and also to set the appropriate transport conditions. This will also support the quality of local food.

The ideal output of this call would be the design of a set of parameters and their ranges and tolerances, which should ensure the quality of transported products from production to sale. The design should also identify the technologies that can participate in this challenge (sensors, cabling, reliable data transmission, data storage, form of data visualization and archiving, ….). It should be a solution that is technologically simple, cheap, user-friendly, and does not impose extensive vehicle modification requirements.

The solution should be applicable to existing resources. It is a comprehensive approach from the point of view of the manufacturer, transporter, and also the developer of the technology.

Introduction/Context

Introduction and Context is the support of small regional food producers (regional specialties) who are specific, unique, and give a special spirit to their region. Unfortunately, they are often very small (even seasonal) producers who do not have the financial and technical resources as large and global market players. From this point of view, it is desirable to preserve the spirit of the regions and to support these regional producers.

The ambition of the challenge

The ambition of the challenge is to design a range of parameters that should be monitored during the transport of food from the producer to the seller. Of course, it should also be a matter of determining their values, ranges, and tolerances.

Next steps

• Design of parameters to be monitored during the transport of regional products from the producer to the seller.
• Design of a measuring chain for measuring specified parameters.
• Design of visualization of measured data.

The registration for the challenges is open! Register for this hackathon challenge HERE.

MENTOR: Michal Kepka

Introduction/Context

Sensor data provides important part of spatial data at all. Especially with spreading of IoT across different domains, huge amount of sensor data can be produced in a short time period. The problem arises with large amount of sensor data collected during observation campaign. The first task is to filter and clean non-valid observations and check periods of data loss or drop-outs. The second task is to prepare initial aggregations of data – hourly/daily/monthly averages, minimums, maximums, sums of selected observed properties. The next step is to prepare analyses based on the target usage of observed data. The last but not the least point is to prepare understandable and usable visualizations of observed and processed data in an explanatory way.

Ambition of the challenge

The challenge should provide extension of the current data flow through the SensLog system and its components to improve the analytical functionality and client-side visualization of observed and analysed data. 

Map window for static sensor nodes

Analytical functions need to reflect current options of data sources – mobile sensors on machinery and delivery pick-ups and large set of static sensor nodes. The set of analytical functions should reflect target domain of the data usage and expected form of analyses. Analytical functions should process collected data as well as integrate data from external sources – remote sensing data, models and simulations. These external datasets can be future observations or predicted values thus the visualization client should be able to distinguish between real and predicted data. 

Visualization of predicted data

Chart visualization for different observed properties of static nodes

Next steps

The challenge will use the SensLog system and its components and data from selected sensor campaigns provided by sensor owners. Challenge task list can be extended by requirements defined by potential end-users of the data flow and client application.

The registration for the challenges is open! Register for this hackathon challenge HERE.

MENTORS: Pavel Hájek, Michal Kepka, Dimitrii Kozhukh

Introduction
Land use and land cover information in combination with other thematic datasets related to detailed reference spatial data in localities forms an important dataset for different analyzes in different domains. Our activity of creating a geographic database OpenLandUse (OLU) aims to be an effective step towards such a model that would effectively gather information about the Earth’s surface in sufficient detail and in sufficiently complex links to be suitable for initiatives such as Green Deal, Destination Earth and the construction of Earth’s twins (Digital Twins).
The OLU 2.0 database combines various thematic data with the most detailed reference geometry available in a given area. Thematic data sets are focused on information on soil cover, soils, topographic or climatic parameters, etc. and in different time periods. The model also supports the possibility of integrating data obtained by evaluating remote sensing data.

Ambition of the challenge

The database covers selected territorial units with a seamless layer, which provides information on various topics on selected reference geometry, which can be a cadastral map, Land Parcel Identification System soil blocks or even elements of the Corine Land Cover data set.

The original purpose of the database was mainly in analyzes for spatial planning and investment, agriculture, landscape development, etc. Our current ambition is to test developed data model and database that support creation of various models of landscape development and scenarios and support the building of large-scale digital models.

The aim of the #8 Challenge is to verify the OLU 2.0 data model is versatile in various areas of human activity, mainly in cooperation with other challenges. Thus we are looking for specialists from various areas of human activity, programmers, remote sensing experts, public administration, land planners, farmers, foresters, nature conservation, real estate experts, business, investments and the like.

Particular goals

• Enhanced the established OLU 2.0 database with other thematic datasets;
• Or with the same thematic datasets as already implemented in OLU 2.0, but for different areas of interest;
• Cooperate with Challenge 5 to be able to incorporate data from sensors into OLU 2.0 as well.

The registration for the challenges is open! Register for this hackathon challenge HERE.

MENTOR: Dmitrij Kožuch

Introduction/Context

The Open Land Use is an initiative of Plan4All association to create seamless land use/ land cover map. At first the initiative was focused on creating OLU map just for Europe filling it just with land use/ land cover data. Later, the extent has changed also to Africa, and the data model has changed to easily integrate other thematic data (soils, geomorphology etc.). 

In the first version of OLU for Africa, just available vector datasets were used: Open Street Map, Africover and some local datasets. The second version used Open Street Map and  S2 prototype land cover 20m map of Africa 2016 from CCI (with pan-African coverage). 

The second version of Open Land Use for Africa was created during Open Spring INSPIRE Hackathon 2021 and the dataset was created mainly based on Open Street Map data and S2 prototype land cover 20m map of Africa 2016 from CCI (Climate Change Initiative).

The goal of the challenge is to improve second version of Open Land Use map for Africa with ESA World Cover data, satellite data and other potential data sources.

The initial attempts were already done. 

The function that uses TerraCatalogue to get World Cover data by given extent is done. Also there has been work done on functions that vectorize data from World Cover, that do computations by tiles (in some large provinces is impossible to do computations without splitting data into data tiles) and than function that merges data tiles.

Some prototype that demonstrates the difference between the second version of OLU for Africa and the new version that incorporates World Cover was created: https://tinyurl.com/yak642nl :

The second version:

The prototype:

As it is seen the advantage of incorporating World Cover is big. Also the incorporation of other data sources including satellite data or some local information, could imporove the result. However, the main goal will be to create the third version of Open Land Use for Africa, that will incorporate World Cover, and the addition of other data sources will be features that are good to have.

Ambition of the challenge

In this challenge the main focus will be to create the third version that will incorporate World Cover dataset by ESA. Additionally, the revision of new suitable data sources will be done.

Next steps

The description of World Cover dataset is given here: https://esa-worldcover.org/en/data-access .

The programming language that is used is Python 3, and the outcome data is stored in PostgreSQL database. The code for creating of the second version of OLU for Africa is available:

https://github.com/xarabiburacaramba/olu_v2/blob/main/plneni_db_olu_v2_afrika_z_osmu_upravy.py 

The prototype code to be used for creation of the third version will be shared soon.

The registration for the challenges is open! Register for this hackathon challenge HERE.

MENTORS: Zuzana Palkova, Miroslav Konecny, Pavol Findura

Introduction/Context

The proposed methodology for identifying problematic areas of a selected crop will result from the measured data, whether in an imagery form through specialized cameras (capable of scanning in the infrared, red, blue and green wavebands), or from sensors. Consequently, the vegetation indexes (Normalized Difference Vegetation Index (NDVI), Enhanced vegetation index (EVI)) allow identifying of problematic areas in the crop and then the anticipated nature, extent and cause of damage will be determined. The system determines the coordinates, suggests procedures, methods and dosage and unmanned devices apply designated substances.

Ambition of the challenge

Possibilities for further development and implementation in agricultural practice are very wide. The ambition of the challenge is to open discussion for further cooperation in the frame of triple helix principle – R&D/farmers/SMEs.

The discussions that the INSPIRE hackathon should bring, allow to clarify some of the requirements of target consumers, primary producers, and the effect of local climate conditions.

During the preparatory activities for the challenge, several questions arose from the discussions, such as:

• Are the NDVI and EVI methods optimal or building reference images databases of different health conditions of plants will be necessary?
• Is there a real interest of farmers to use drones for the health and nutritional condition of crops diagnosis, considering the limitations for using drones?
• What will be the business model for further exploitation of developed applications?

The main points for the next INSPIRE Hackathon include more precise specifications of the farmers’ needs, possibilities for plants’ image database development, and clarification of the business potential of drones utilization in agriculture practice.

The registration for the challenges is open! Register for this hackathon challenge HERE.

MENTOR: Jakub Fuska

The challenge is focused on the improvement of current version of system designed in first hackatho and further works. This system is calculating the water reservoir storage capacity based on the DEM of reservoir bottom and current water level with the incorporating the water reservoir watershed parameters and precipitation forecast. The input data are processed and stored in existing WebGIS server platforms (GeoServer, PostGIS).

Current system description:

• input data: Reservoir bottom DEM, Reservoir watershed DEM, land cover of watershed; data is stored in PostGIS
• database of water level simulations (substitution for actual sensors)
• database of soil moisture simulations (substitution for actual sensors)
• connection to openweathermap to provide the weather forecast data

What we expect from participants during the hackathon:

• general insight on the created system, discussion, proposals of improvement, new functionality
• creation of the of the hydrological modelling – new or existing models implementation and improvement in accordance to available data
• new or improved data inputs – weather forecast, soil maps, land cover, etc.

Introduction/Context

As the farmers are pumping out the water from reservoir, the water storage is decreasing and the water level is descending. The water level elevation can be surveyed with existing prototype of ultrasonic sensor that stores the currently measured and calculated water level elevation in database and with the use of reservoir DEM we can calculate the current amount of water in reservoir.

As the longer periods of droughts are presumed worldwide (climate change in progress), the amount of available irrigation water is crucial. If we would be able to incorporate the data from watershed (watershed terrain slopes, land use, soil types, etc.) and the prediction of precipitation (weather forecast), we can predict the amount of water that will flow to reservoir during drought period and during/after rain event. This would help the farmer to manage the usage of remaining water amount in reservoir by altering the uptake of water for irrigation.

Ambition of the challenge

Challenge is aiming both on introducing the concept to the broader audience of experts both to gain the new insight to this topic and to spread the concepts idea to incorporate its principles and solutions to the further incorporating in research topics and/or in practical applications in real situations.

Next steps

• spread the information
• gather the information to improve the system (technical, general)
• provide the opportunity to test the own solutions of hydrological modelling

Tools and applications:

• system i currently working on infrastructure of virtual server of company ATAPEX (PostGIS database, Geoserver, Python server), the sensors of soil moisture are operated by company PlantControl, weather forecast is provided by openweathermap. Org API

Used data:

• Reservoir bottom DEM, Reservoir watershed DEM, land cover of watershed; data is stored in PostGIS
• database of water level simulations (substitution for actual sensors)
• database of soil moisture simulations (substitution for actual sensors)
• connection to openweathermap to provide the weather forecast data

The registration for the challenges is open! Register for this hackathon challenge HERE.

MENTORS: Marcela Bindzarova Gergelova, Martin Tuchyňa

Introduction

Challenge will build on the experience presented and documented so far. For Agrihub INSPIRE Hackathon 2022, challenge will aim to enlarge data offerings from public sector, voluntary geographic communities as well as from private sector providers. In the area of use cases collection, challenge will prepare structure for their collection and publishing. Similarly relevant stakeholders shall be identified, documented, and where possible connected. Where real stakeholders, willing to cooperate will be identified, relevant user requirements will be identified and supported via selected use case/s.  

Ambition

For identified farmers and other related stakeholders, the challenge will prepare an initial set of available datasets via Agrihub.sk platform.

In parallel, requirements and relevant use cases will be collected. These requirements and use cases will be collected from connected stakeholders as well as by the members of the challenge team. From these collected use cases those with potential to pilot will be further developed. Among the first candidates might be those helping to:

• Visualize farmers borders of the fields;
• Plan farmers optimal tracks for the field operations;
• Share information about the cultivated crops.

Challenge will also aim to identify additional information technologies and their use in order to support agricultural and environmental challenges. 

Next steps

In order to achieve above mentioned ambition following steps are foreseen:

1. Recall of the team from the Agrihub INSPIRE Hackathon 2021 + collect new members of the team
2. Identification of the representatives of target stakeholders
3. Collect requirements and use cases  
4. Identify relevant datasets 
5. Finetune challenge/s with stakeholders’ requirements
6. Implement possible actions to address requirements
7. Validate outcomes with stakeholders
8. Document the results

The registration for the challenges is open! Register for this hackathon challenge HERE.