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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 understand 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 propose 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 generate 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-temporal 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.
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This content will become publicly available on October 1, 2025
A method for defining dispersed community territories
The transition from dispersed to aggregated forms of settlement reflects a critical shift in the relative value of social and primary (food) modes of production. However, investigating trade-offs between these different forms of settlement requires estimates of the extent of community territories, including their nearby arable land. Here we demonstrate a simple algorithm to do that. Our algorithm is analogous to that used to define core-based statistical areas for the US census, though instead of central business districts, we rely on community centers (or areas of known and persistent interaction between unrelated individuals). We provide examples of our algorithm by applying it to archaeological sites in the central Mesa Verde, northern Rio Grande, and Cibola regions in the US Southwest. A sensitivity analysis is also conducted to demonstrate how each tuning parameter contributes to the algorithm.
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- Award ID(s):
- 2213921
- PAR ID:
- 10565262
- Publisher / Repository:
- Journal of Archaeological Science
- Date Published:
- Journal Name:
- Journal of Archaeological Science
- Volume:
- 170
- Issue:
- C
- ISSN:
- 0305-4403
- Page Range / eLocation ID:
- 106048
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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