Open land-use map: a regional land-use mapping strategy for incorporating OpenStreetMap with earth observations

Abstract

A land-use map at the regional scale is a heavy computation task yet is critical to most landowners, researchers, and decision-makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating land classification maps at the regional scale: the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time. Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens. As one of the most well-known VGI initiatives, OpenStreetMap (OSM), contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns. Whereas, most large-scale mapping approaches – including regional and national scales – confuse “land cover” and “land-use”, or build up the land-use database based on modeled land cover data-sets, in this study, we clearly distinguished and differentiated land-use from land cover. By focusing on our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery. Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work. Furthermore, our novel approach in regional scale land-use mapping produced robust results in our study area: the overall internal accuracy of the classifier was 95.2% and the external accuracy of the classifier was measured at 74.8%.

Keywords:

• OpenStreetMap (OSM)
• Volunteered Geographic Information (VGI)
• crowdsourced data
• Random Forest
• Google Earth Engine (GEE)

Acknowledgments

We would like to thank the Google Earth Engine Development team for input and assistance with the API platform.