Using Citizen Science to Help Monitor Urban Landscape Changes and Drive Improvements

Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues. LandSense, a Horizon 2020 project that is deeply rooted in environmental challenges and solutions, aims to establish a citizen observatory that will provide data to stakeholders, from researchers to businesses. Within this project, a mobile application has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. The launch of a public version of such an app requires preparation and testing by focus groups. Recently, a prototype of the app was used by both staff and students from Vienna University of Technology, who contributed valuable insights to help enhance this citizen science tool for engaging and empowering the inhabitants of the city

The CAMALIOT project

This invited presentation was given at an information event about the European Space Agency’s (ESA) Navigation Innovation and Support Programme (NAVISP) hosted by the Austrian Agency for the Promotion of Science (FFG) in preparation for the ESA Ministerial Conference 2022. The presentation was about the CAMALIOT project, which is currently funded through NAVISP and by FFG, outlining the initial results and what the next steps in the project are. In particular, information about the CAMALIOT crowdsourcing campaign (being run by IIASA) was provided as well as the status of the CAMALIOT machine learning infrastructure and the science uses cases in the project

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations

Validating maps of land cover and land degradation with citizen science and mobile gaming

Peatland comprises around 24% of South Sumatra, a province on the island of Sumatra in Indonesia. Following catastrophic fires in 2015, peat restoration has become a priority for this area. To identify candidate areas for restoration, both land cover over time and land degradation have been mapped using optical and radar remote sensing. Limited field data have been used to help validate these maps but more validation data are still needed. One way to fill this gap is to tap into the power of citizen science, which has become an emerging area of interest. In citizen science, any member of the public can take part in scientific research, whether this is through data collection, analysis of the data or hypothesis generation. Here we present the results from a citizen science campaign using the Urundata mobile gaming application, which has been developed as part of the Restore+ project. Urundata has two main components: a rapid image assessment tool that allows users to classify satellite imagery by the type of land cover/land use visible or to examine pairs of images for detection of change over time (developed from an application called Picture Pile). The second component sends users to specific locations on the ground via a mobile device and asks for information related to land cover and evidence of land degradation (developed from an application called FotoQuest Go). Together these two components have been used to help validate land cover and land degradation maps of South Sumatra through citizen science

The Picture Pile Tool for Rapid Image Assessment: A Demonstration using Hurricane Matthew

In 2016, Hurricane Matthew devastated many parts of the Caribbean, in particular the country of Haiti. More than 500 people died and the damage was estimated at 1.9billionUSD. At the time, the Humanitarian OpenStreetMap Team (HOT) activated their network of volunteers to create base maps of areas affected by the hurricane, in particular coastal communities in the path of the storm. To help improve HOT’s information workflow for disaster response, one strand of the Crowd4Sat project, which was funded by the European Space Agency, focussed on examining where the Picture Pile Tool, an application for rapid image interpretation and classification, could potentially contribute. Satellite images obtained from the time that Hurricane Matthew occurred were used to simulate a situation post-event, where the aim was to demonstrate how Picture Pile could be used to create a map of building damage. The aim of this paper is to present the Picture Pile tool and show the results from this simulation, which produced a crowdsourced map of damaged buildings for a selected area of Haiti in 1 week (but with increased confidence in the results over a 3 week period). A quality assessment of the results showed that the volunteers agreed with experts and the majority of individual classifications around 92% of the time, indicating that the crowd performed well in this task. The next stage will involve optimizing the workflow for the use of Picture Pile in future natural disaster situations