Enhanced urban functional land use map with free and open-source data

ABSTRACT

The study aims at developing an applicable methodology to produce the functional land-use map using only free and open-source data. Top-view Sentinel image and ground-view Open Street Map (OSM) data are chosen due to their extensive availability. The three-stage framework, including object-based image analysis, OSM data cleaning, and ontology-based decision fusion, is proposed and implemented with open-source tools. We applied the developed approach to districts 1, 4, and 7 of HoChiMinh city, representing the complexities of the dynamic change in big cities. The result showed a good functional land use map with 78.70% overall accuracy. The outcome presents the mismatch between the data-driven approach and human knowledge, which can be improved by ontology-based fusion with OSM data. The ontology-based framework comprises the common urban land-use classes and OSM attributes, which can be applied and extended in other urban areas. Additional text attributes may be applicable only locally and can be modified in our open-source framework. Object-based image analysis takes advantage of Google Earth Engine computing power, whereas ontology-based processing works well on a local computer. In future studies, adopted natural language processing to pre-process OSM data and ontology-based fusion will be implemented on the cloud-computing platform to enhance computational efficiency.

KEYWORDS:

• Data fusion
• Remote Sensing
• Digital City
• Land use
• GIS

Acknowledgments

The authors are grateful for the data and tool support from Google Earth Engine and Open Street Map (via Geofabrik) platforms.

Data availability statement

The data that support the findings of this study are available on Google Earth Engine (https://earthengine.google.com) and Geofabrik (https://www.geofabrik.de).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The study is a part of the research project ‘Enhance the quality of remote-sensing-derived information with crowd-sourced data’ [102.99-2018.16] funded by The National Foundation for Science and Technology Development (NAFOSTED), Vietnam.