ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-L-4-W1-2026-275-2026 Spatiotemporal Analysis of OpenStreetMap Editing Activities in Japan Using the OSMCha Seto Toshikazu https://orcid.org/0000-0003-0281-1790 1 2 Department of Geography, Komazawa University, Tokyo, Japan 3D Geoinformation group, Department of Urbanism, Faculty of Architecture and Built Environment, Delft University of Technology, Delft, The Netherlands 29 08 2026 L-4/W1-2026 275 282 Copyright: © 2026 Toshikazu Seto 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/L-4-W1-2026/275/2026/isprs-archives-L-4-W1-2026-275-2026.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/L-4-W1-2026/275/2026/isprs-archives-L-4-W1-2026-275-2026.pdf

OpenStreetMap (OSM) is a prominent platform for Volunteered Geographic Information (VGI), wherein geographic data are updated daily by a global community of contributors. The OpenStreetMap Changeset Analyzer (OSMCha) serves as a quality assurance tool that automatically flags suspicious edits based on detection rules covering geometric and tag plausibility, edit scale, and contributor behavioral patterns. In this study, we developed Python scripts to systematically collect 740,038 changesets spanning four years (2022&ndash;2025) for Japan via the OSMCha API, consolidizing them into a FlatGeobuf database with 60 reason_id mappings embedded as attributes and prefecture-level spatial joins applied.&nbsp;<br />Our analysis revealed a 63.3% increase in annual changesets and a 72.5% expansion in unique contributors, alongside an 11.9- percentage-point decline in the suspicious edit rate. Mobile editors and survey-based edits grew rapidly, consistently demonstrating lower suspicion rates than non-survey edits. A structural shift in the OSMCha detection logic was empirically identified in 2024. The KDE analysis confirmed editing hotspots in three major metropolitan areas, while the January 2024 Noto Peninsula earthquake triggered concentrated crisis mapping, engaging 1,536 contributors.