This is a joint progress report of teams no. 1, 5, 6 and 8 of the Nairobi INSPIRE Hackathon 2019.
Effective environmental management – including day-to-day, hazard and crisis management – is dependent on effective environmental monitoring. But monitoring over large areas in-situ often requires higher levels of capacity than monitoring bodies have. At the same time, those measurements that we can get over large areas – the Earth Observation data coming from satellites – may need in-situ validation in order to provide sufficiently predictive models for meaningful management. Again this can require very high levels of capacity. In both cases, Citizen Science (CS) has already emerged as a potential solution, as exemplified by projects such as beAWARE, Ground Truth 2.0, and a whole range of innovation initiatives – including INSPIRE’s hackathons.
Nairobi INSPIRE Hack is trying to bring new quality and aspects into Citizen Science (CS), extended current understanding of CS and ensuring that CS data can be interoperable with other systems and data relevant to Environmental and Spatial Data Infrastructure applications. From existing teams, which tested some concepts during Nairobi INSPIRE Hackathon team, we design more complex and open solution, which will support better utilisation of CS potential. We defined basic scheme, which can be base for future complex CSsolution.
Core component will be Digital Innovation Hub (DIH) SmartAfriHub. Digital Innovation Hubs (DIHs) ensure the connection between the ICT and the communities by bringing together IT suppliers, the users as foresters and farming sector, technology experts, investors and other relevant actors. This leads to new applications that are adapted to the real needs of communities. The intention of Nairobi INSPIRE Hack is to develop a social space for African agriculture and forestry, where farmers, forest owners, the industry, research community, advisory services and others will be able share their knowledge, needs and experience.
From the scheme is visible, that SmartAfriHub is focused not only on sharing documents, but main focus is on sharing different spatial information. Important part of all infrastructure is Geospatial Catalogue Micka (see progress report focused on this aspects). This catalogue supports harvesting metadata for spatial data and services from different existing sources like FAO, GEOSS etc and builds one access point for all communities to this services. Micka is using common standards, but there is one important extension, which can introduce a new concept of Citizen Science. This is Map Composition.
Map Composition and Citizen Science objects
Today we propose to you the idea that “maps” are interesting not only as visualizations of Citizen Science data capturing through semi-voluntary projects — but as shareable, fascinating and valuable Citizen Science objects in themselves.
Citizen Science has until now been very much about trying to structure data from Internet users into harmonized (or semi-harmonized) data sets such as the hugely successful Open Street Map and the like. Users contribute points, lines and polygons as well as their meaning. Similarly, services like Instagram capture image data and exposes it through APIs for use in a multitude of serendipitous, and not so serendipitous ways.
So why do we believe that the “map” is an object that people would like to share — and why do we believe that it proves added value to people’s lives?
- All of us have tried to explain a location to someone over the phone
- All of us have asked for advice from well-meaning but “spatially challenged” staff in hotel receptions
- All of us have tried to discuss what we believe to be the same location with a colleague over the phone
- Some of us have tried to explain a colleague how to add a WMS theme to an interactive map client
All of us, at one stage or another, without exception have failed at these tasks. Some times miserably.
This is where the “map” comes to play. 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 11 years that have passed since its creation.
The current specification of the JSON Map Composition is available on the GitHub Wiki of HSLayers NG. We believe Map Compositions have a wide range of private and professional use cases and would like to use the Nairobi INSPIRE Hack to extend this concept. To ensure that Map Compositions play a positive role in the future of spatial data, we we need to establish through examples and trials how they:
- provide societal and/or economic benefit
- serve existing, current or foreseeable needs
- fit into existing processes
- replaces existing processes
Current solution allow not only search for Map composition on Hub, but for example also share this composition through Social Media.
or embed this compositions into Web pages as interactive maps
Metasearch plugin for QGIS
Metasearch plugin extends function of QGIS about loading Map compositions from innovation Hub using standard CSW. It is opening possibilities for validation existing data, results of classification and other services on DIH on desktops and allows better utilisation of existing data.
Data on DiH
Search for existing data and services on DiH from QGIS
and then open map composition on QGIS
Next step, which is now under development is storing new maps and components on DiH directly from QGIS. This will be realised through Layman component of DiH.
This will bring new methods into citizens science and will support better interaction with DiH. It will help annotate existing classification, but also prepare training samples for classification of Earth Observation Data.
Earth Observation Module
For Earth Observation module we use virtual Cloud server managed by Plan4All. Cloud is based on Open Stack and as software for processing images we select Orfeo ToolBox – Open Source processing of remote sensing images. There will be used as input data terrestrial data from Citizens Science APPS and also data prepared by QGIS. Linkage of Orfeo ToolBox will be also provided for better interaction. We also start downloaded some data from region. See examples:
Data will be available also as WMS services.
Integration of Existing Citizens Science data
There are many use cases where satellite imagery needs to be validated by people on site. Some are related to environmental challenges, some can be oriented on fighting crime and we are sure there are many more, but one thing they mostly have in common: position needs to be specified and described by qualitative and quantitative means.
During Inspire hackathon we have developed a configurable platform (demo at https://groundtruth.plan4all.eu/) for input and storage of point features together with description, time dimension, some classification means and possibility to upload image of a earth observation, which the community then validates. The validation is done by voting (approve / disapprove), providing more precise measurement in numerical form and uploading additional photos and/or comments.