Analyzing the Behaviors of OpenStreetMap Volunteers in Mapping Building Polygons Using a Machine Learning Approach

Abstract

Mapping as an action in volunteered geographic information is complex in light of the human diversity within the volunteer community. There is no integrated solution that models and fixes all data heterogeneity. Instead, researchers are attempting to assess and understand crowdsourced data. Approaches based on statistics are helpful to comprehend trends in crowd-drawing behaviors. This study examines trends in contributors’ first decisions when drawing OpenStreetMap buildings. The proposed approach evaluates how important the properties of a point are in determining the first point of building drawings. It classifies the adjacency types of the buildings using a random forest classifier for the properties and aids in inferring drawing trends from the relative impact of each property. To test the approach, detached and attached building groups in Istanbul and Izmir, Turkey, were used. The result had an 83% F-score. In summary, the volunteers tended to choose as first points those further away from the street and building centroid and provided lower point density in the detached buildings than the attached ones. This means that OSM volunteers paid more attention to open spaces when drawing the first points of the detached buildings in the study areas. The study reveals common drawing trends in building-mapping actions.

Georeferencing for Planning Underground Networks

“…A relationship is found between length, street type, and parking zone ob-tained [20], [21]. The relationship between cartography and urban management provides an option for the study of trends for decision-making by professionals in charge of drawing buildings with data from OpenStreetMap (OSM) [22]. The management of geo-referenced information may include information that needs to be verified due to possible failures in the on-site visit; previous works evaluate labels and filters to evidence buildings in specific areas [23].…”

“…Objectives Electricity Drinking Water Data Gas Transport Other Garcia, 2023 [6] Power Network Planning ✓ ✓ Kim, 2023 [16] Healthcare accessibility ✓ ✓ ✓ Wu, 2023 [15] Urban Development ✓ ✓ ✓ Gaugl, 2023 [14] Power Network Planning ✓ ✓ Kersapati, 2023 [17] Urban Management ✓ ✓ ✓ Kim, 2022 [25] Small unmanned aircraft ✓ ✓ Song, 2022 [23] Remote sensing -Urban planning ✓ ✓ Hacar, 2022 [22] Urban planning ✓ ✓ Hellekes, 2022 [20] Urban Planning ✓ ✓ ✓ Zourlidou, 2022 [19] Traffic Engineering ✓ ✓ ✓ Milleville, 2022 [24] Gerreferencing…”

Metamorphic Testing of Advanced Driver-Assistance Systems: Implementing Euro NCAP Standards on OpenStreetMap

“…These maps provide rich environmental information, such as static roads, buildings, and traffic infrastructures. The environmental information is useful for machine learning approaches [15], and for the vehicle to understand the driving functionalities, therefore contributing to accessing the automated vehicle behaviors.…”

Effects of reverse geocoding on OpenStreetMap tag quality assessment

“…Unlike commercial mapping platforms like Google Maps and Bing Maps, OSM allows both volunteers and non‐experts to contribute to mapping through online interfaces (Goodchild, 2007; Heipke, 2010). The number of registered users for the OSM project is steadily increasing, and there is a wealth of research highlighting the participants and their contribution trends (Arsanjani et al, 2015; Hacar, 2022; Neis & Zielstra, 2014; Neis & Zipf, 2012). This emphasizes the importance of evaluating the geometric, topological, and semantic quality of the map features added and edited in VGI before using them in research (Haklay, 2010).…”