Towards a framework for measuring local data contribution in OpenStreetMap

Owusu, M., Herfort, B. & Lautenbach, S. (2021). Towards a framework for measuring local data contribution in OpenStreetMap In: Minghini, M., Ludwing, C., Anderson, J., Mooney, P., Grinberger, A.Y. (Eds.). Proceedings of the Academic Track at the State of the Map 2021 Online Conference, July 09-11 2021, 16-18. Available at https://zenodo.org/communities/sotm-202

Understanding MapSwipe: Analysing Data Quality of Crowdsourced Classifications on Human Settlements

Geodata is missing to populate maps for usage of local communities. Efforts for filling gaps (automatically) by deriving data on human settlements using aerial or satellite imagery is of current concern (Esch et al., 2013; Pesaresi et al., 2013; Voigt et al., 2007). Among semi-automated methods and pre-processed data products, crowdsourcing is another tool which can help to collect information on human settlements and complement existing data, yet it’s accuracy is debated (Goodchild and Li, 2012; Haklay, 2010; Senaratne et al., 2016). Here the quality of data produced by volunteers using the MapSwipe app was investigated. Three different intrinsic parameters of crowdsourced data and their impact on data quality were examined: agreement, user characteristics and spatial characteristics. Additionally, a novel mechanism based on machine learning techniques was presented to aggregate data provided from multiple users. The results have shown that a random forest based aggregation of crowdsourced classifications from MapSwipe can produce high quality data in comparison to state-of-the-art products derived from satellite imagery. High agreement serves as an indicator for correct classifications. Intrinsic user characteristics can be utilized to identify consistently incorrect classifications. Classifications that are spatial outliers show a higher error rate. The findings pronounce that the integration of machine learning techniques into existing crowdsourcing workflows can become a key point for the future development of crowdsourcing applications

The tasks of the crowd : a typology of tasks in geographic information crowdsourcing and a case study in humanitarian mapping

In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, and how the quality of the produced information can be assessed for particular task types. To fill this gap, we analyse the existing literature and propose a typology of tasks in geographic information crowdsourcing, which distinguishes between classification, digitisation and conflation tasks. We then present a case study related to the “Missing Maps” project aimed at crowdsourced classification to support humanitarian aid. We use our typology to distinguish between the different types of crowdsourced tasks in the project and choose classification tasks related to identifying roads and settlements for an evaluation of the crowdsourced classification. This evaluation shows that the volunteers achieved a satisfactory overall performance (accuracy: 89%; sensitivity: 73%; and precision: 89%). We also analyse different factors that could influence the performance, concluding that volunteers were more likely to incorrectly classify tasks with small objects. Furthermore, agreement among volunteers was shown to be a very good predictor of the reliability of crowdsourced classification: tasks with the highest agreement level were 41 times more probable to be correctly classified by volunteers. The results thus show that the crowdsourced classification of remotely sensed imagery is able to generate geographic information about human settlements with a high level of quality. This study also makes clear the different sophistication levels of tasks that can be performed by volunteers and reveals some factors that may have an impact on their performance

Crowdsourced validation and updating of dynamic features in OpenStreetMap an analysis of shelter mapping after the 2015 Nepal earthquake

The paper presents results from a validation process of OpenStreetMap (OSM) rapid mapping activities using crowdsourcing technology in the aftermath of the Gorkha earthquake 2015 in Nepal. We present a framework and tool to iteratively validate and update OSM objects. Two main objectives are addressed: first, analyzing the accuracy of the volunteered geographic information (VGI) generated by the OSM community; second, investigating the spatio-temporal dynamics of spontaneous shelter camps in Kathmandu. Results from three independent validation iterations show that only 10 % of the OSM objects are false positives (no shelter camps). Unexpectedly, previous mapping experience only had a minor influence on mapping accuracy. The results further show that it is critical to monitor the temporal dynamics. Out of 4,893 identified shelter camps, 54% were already empty/closed six days after the first mapping. So far, updating geographical features during humanitarian crisis is not properly addressed by the existing crowdsourcing approaches

Being specific about geographic information crowdsourcing : a typology and analysis of the Missing Maps project in South Kivu

Recent development in disaster management and humanitarian aid is shaped by the rise of new information sources such as social media or volunteered geographic information. As these show great potential, making sense out of the new geographical datasets is a field of important scientific research. Therefore, this paper attempts to develop a typology of geographical information crowdsourcing. Furthermore, we use this typology to frame existing crowdsourcing projects and to further point out the potential of different kinds of crowdsourcing for disaster management and humanitarian aid. In order to exemplify its practical usage and value, we apply the typology to analyze the crowdsourcing methods utilized by the members of the Missing Maps project developed in South Kiv

3D-MAPP: 3D-MicroMapping von großen Geodatensätzen im Web

Die automatische Detektion von Objekten in 3D-Geodaten ist ein wichtiger Bestandteil vieler GIS-Workflows, sei es bei der Kartierung geomorphologischer Formen (Bremer, Sass 2012; Rutzinger et al. 2012) Austrian Alps, using a combination of terrestrial (TLS, der Generierung von 3D-Stadtmodellen (Niemeyer et al. 2012) oder der Entwicklung autonom agierender Fahrzeuge (Maturana, Scherer 2015). Besonders in städtischen Räumen, welche durch komplexe Objektstrukturen sowie eine Vielzahl an verschiedenen Objekttypen charakterisiert sind, können automatische Methoden allein jedoch selten zufriedenstellende Ergebnisse liefern. In diesem Beitrag möchten wir daher untersuchen, inwieweit nutzergenerierte Geodaten bzw. sogenanntes „MicroMapping“ Ansatzpunkte für die Lösung des beschriebenen Problems sein können. Im Rahmen des 3D-MAPP-Projektes wurden dazu 3D-MicroMapping-Aufgaben mit einer unterschiedlichen Komplexität entworfen und in einer Webanwendung implementiert. Die Anwendbarkeit der Methode wurde anschließend in einer empirischen Nutzerstudie untersucht. In der Studie wurden segmentierte LiDAR-Punkwolken genutzt, welche Bäume im städtischen Raum abbilden. Die Aufgabe für die Teilnehmer der Studie bestand darin, Informationen zur Höhe der Baumkrone, zu fehlenden Teilen der Bäume und zu weiteren in der Punktwolke abgebildeten Objekten zu erfassen

Twitter Analysis of River Elbe Flood

ABSTRACT In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring

Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned

In the past few years, crowdsourced geographic information (also called volunteered geographic information) has emerged as a promising information source for improving urban resilience by managing risks and coping with the consequences of disasters triggered by natural hazards. This chapter presents a typology of sources and usages of crowdsourced geographic information for disaster management, as well as summarises recent research results and present lessons learned for future research and practice in this field

Leveraging OpenStreetMap to support flood risk management in municipalities : a prototype decision support system

Floods are considered the most common and devastating type of disasters world-wide. Therefore, flood management is a crucial task for municipalities- a task that requires dependable information to evaluate risks and to react accordingly in a disaster scenario. Acquiring and maintaining this information using official data however is not always feasible, especially for smaller municipalities. This issue could be approached by integrating the collaborative maps of OpenStreetMap (OSM). The OSM data is openly accessible, adaptable and continuously updated. Nonetheless, to make use of this data for effective decision support, the OSM data must be first adapted to the needs of decision makers. In the pursuit of this goal, this paper presents the OpenFloodRiskMap (OFRM)- a prototype for a OSM based spatial decision-support system. OFRM builds an intuitive and practical interface upon existing OSM data and services to enable decision makers to utilize the open data for emergency planning and response