Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better un-derstand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we pro-pose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically gen-erate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-tem-poral GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.
|Original language||English (US)|
|State||Published - Sep 2021|
Bibliographical noteFunding Information:
Funding: This research was funded by National Science Foundation (Grants #1563933, #1564164, and #1924670), as well as by the National Institutes of Health (Grant #P2CHD066613).
Acknowledgments: This material is based on research sponsored by the National Science Foundation (NSF, IIS 1563933 to the University of Colorado at Boulder and IIS 1564164 to the University of Southern California). It is also supported in part by NSF Award 1924670 and the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (Award P2CHD066613). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Publication of this article was funded by the University of Colorado Boulder Libraries Open Access Fund.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Built-up land data
- Data integration
- Global human settlement layer
- Historical maps
- Long-term settlement patterns
- Topographic map processing