Towards the development and verification of a 3D-based advanced optimized farm machinery trajectory algorithm

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostenice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories

Geografická informace v době směrnice INSPIRE :nalezení, získání a využití dat pro geografický výzkum

This article describes the concept and impacts of dealing with geographic information according to the Directive on INfrastructure for SPatial InfoRmation in Europe: INSPIRE. A brief introduction contains the scope and aims of this infrastructure and is supported by a section on legislative background at both the European and Czech levels. All components of the European infrastructure are analysed sequentially, i.e. starting with metadata, network services, data sharing, monitoring and reporting. The main focus of the article is aimed at the issues of geographic data interoperability and harmonization through application schemas. Unique identifiers, voidable elements, reference systems, temporal representations, quality of geographic data, encoding and visualisation are then subjected to deeper analysis. The concept of INSPIRE is presented in a model case of searching for cross-border geographic data using the INSPIRE geoportal, its preview in a Geographic Information System and its retrieval.779

Cartographic Visualisation of Data Measured by Field Harvesters

Yield is one of the primary concerns for farmers, as it is the basis for their income and, among other impacts, influences subsidies and taxes. Field harvesters equipped with sensors and a GNSS (Global Navigation Satellite System) receiver provide detailed and spatially localised values, where the measurements from such sensors need to be filtered and subject to further processing, including interpolation, for follow-up visualisation, analysis and interpretation. These data, their processing and their application are some of the aspects of the precision agriculture concept. This paper describes the individual steps of processing the data acquired by harvesters, which especially include the spatial filtering of these data and their interpolation. We also proposed a scheme that summarises cartographic visualisation methods for these data (final data, as well as data from different processing steps). Methods of processing and cartographic visualisation were verified in the example of the Pivovárka field (Rostěnice farm, Czech Republic). Both 2D and 3D cartographic visualisations were created. Future development of the proposed concept is discussed in the conclusions

Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets

Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon’s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met

The Design and Testing of 3DmoveR: an Experimental Tool for Usability Studies of Interactive 3D Maps

Various widely available applications such as Google Earth have made interactive 3D visualizations of spatial data popular. While several studies have focused on how users perform when interacting with these with 3D visualizations, it has not been common to record their virtual movements in 3D environments or interactions with 3D maps. We therefore created and tested a new web-based research tool: a 3D Movement and Interaction Recorder (3DmoveR). Its design incorporates findings from the latest 3D visualization research, and is built upon an iterative requirements analysis. It is implemented using open web technologies such as PHP, JavaScript, and the X3DOM library. The main goal of the tool is to record camera position and orientation during a user’s movement within a virtual 3D scene, together with other aspects of their interaction. After building the tool, we performed an experiment to demonstrate its capabilities. This experiment revealed differences between laypersons and experts (cartographers) when working with interactive 3D maps. For example, experts achieved higher numbers of correct answers in some tasks, had shorter response times, followed shorter virtual trajectories, and moved through the environment more smoothly. Interaction-based clustering as well as other ways of visualizing and qualitatively analyzing user interaction were explored

Humanitarian Mapping as a Contribution to Achieving Sustainable Development Goals: Research into the Motivation of Volunteers and the Ideal Setting of Mapathons

Missing Maps is a humanitarian mapping project that maps vulnerable places in the developing world. Its outcomes are used to target aid in affected areas and to help achieve Sustainable Development Goals. A mapathon is an event in which a group of volunteers maps a defined location. The presented communication answers the following questions: What is the motivation of different contributors in the Missing Maps community in Czechia and Slovakia? How can a mapathon be set up to attract as many participants as possible? How exactly can the contributors to humanitarian mapping subjectively evaluate their contribution so far? A questionnaire about the motivation of contributors and the analysis of statistics from eighteen public mapathons in Brno (Czechia) were used as the primary research methods. The analysis of motivation found six strong motivators. Half of them concern altruism and half of them relate to the importance of the OpenStreetMap project and the mapping community. Analysis of the characteristics of 18 mapathons found that the month of the mapathon had a significant influence on the number of attendants. Statistical analysis confirmed a significant correlation between the number of edits and participants’ self-assessment. This means that humanitarian mappers evaluate their overall contribution very realistically. Analyses with an identical scope are planned for future years

Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations

An Advanced Open Land Use Database as a Resource to Address Destination Earth Challenges

Land-use and land-cover (LULC) themes are important for many domains, especially when they process environmental and socio-economic phenomena. The evolution of a land-use database called Open Land Use (OLU) started in 2013 and was continued by adapting many user requirements. The goal of this study was to design a new version of the OLU database that would better fit the gathered user requirements collected by projects using LULC data. A formal definition of the developed data model through Unified Modeling Language (UML) class diagrams, a feature catalogue based on ISO 19110 and SQL scripts for setting up the OLU database, are the key achievements of the presented paper. The presented research provides a multi-scale open database of LULC information supporting the DestinE initiative to develop a very-high-precision digital model of the earth. The novel spatio-temporal thematic approach also lies in modular views of the OLU database