FOSS4G 2023

SMASH , the digital field mapping application for android and IOS that superseded the well known app geopaparazzi has been around for some years now. The last two years were a positive development storm after a quite calm year and brought many fixes as well as enhancements. Examples are better postgis and geopackage support, but also some hidden gems like geocaching.
The big news is on the serverside though. A new survey server has been developed in tight cooperation with a local government agency to best create effective surveying workflows and tools for survey teams. To attract a wider developer community to contribute to the project, the django framework was chosen for the server backend.
This presentation will give an overview of everything happened lately in the SMASH field mapping world.

DigiAgriApp is a client-server application to manage different kinds of data related to farming fields. It is able to store information about crops (specie, farming forms/system...), any kind of sensor data (included sensors and device hardware, weather, soils...), irrigation information (system type, openings...), field operations (pruning, mowing, treatments...), remote sensing data (taken from different devices as mobiles, drone, satellites) and production quantities.

The DigiAgriApp server is composed of a PostgreSQL/PostGIS database and a REST API service to interface with it. The server is developed using Django and the Django REST framework extension with other minor extensions are used to create the REST API. This service plays the key interface between the database and the client. We choose a nested way to create the API, of which the main element is the farm; this way the user can see only the farms related to him and from there he can look to other nested elements, first of all the farm’s fields and later other elements like sensor and remote data or other sub-fields like rows and plants. The REST API is using JavaScript Object Notation as input and output format to simplify and standardize the communication with it.

To obtain data from the sensors the server is also composed of a growing number of services to work with data providers, of which currently only a few are implemented. The Message Queue Telemetry Transport provider is a demon listening continuously to a broker (backend system to coordinate different clients) and several topics to obtain data as soon as they are provided; the second provided that is already implemented is related to remote sensing data and uses the SpatioTemporal Asset Catalogs specification to obtain the data. STAC is a common language to describe geospatial information, so it can more easily be worked with, indexed and discovered.

The client side instead is developed using Flutter, an open-source UI software development kit based on dart, a programming language designed for client development. Flutter is able to create cross-platform applications and it was chosen precisely because of its ability to realize cross platform applications.

All the code is released as Free and Open Source software with a GNU General Public License Version 3 license; it is available in the DigiAgriApp repository on GitLab and the client application will be published also in the main stores for mobile apps.

The Knowledge Laboratory, in short k.LAB, is a software stack that embraces the FAIR principles: findable, accessible, interoperable and reusable. Its objective is to support linked knowledge across the borders of the domains of single modelers and scientists. k.LAB’s fascinating novelty is the use of semantics to create a natural language to describe the models and the qualities that want to be observed.

Modelers can develop their models and publish them to the network. Publishing makes them findable and accessible within the network. Since everything in the network is observable, when running a model, k.LAB looks for the best knowledge unit able to resolve the particular request. Interoperability is build and reusability is a natural consequence.

The k.LAB software stack is free and open source and relies on various projects of the Osgeo community as Geoserver, Openlayers and the Hortonmachine. It has been in development for almost 2 two decades and got a particular visibility boost in 2021, when the Statistics Division of the UN Department of Economic and Social Affairs and the UN Environment Program, in collaboration with the Artificial Intelligence for Environment & Sustainability at the Basque Centre for Climate Change, launched the Artificial Intelligence powered application for rapid natural capital accounting: the ARIES for SEEA Explorer.

Lately a python client that allows interaction with k.LAB has been released. This opens up to new ways to observe the world from within common GIS tools as for example QGIS.

An overview of the state of the art of the project will be given.