Automated earthwork detection using topological persistence

Authors: LAPIDES, D.A. - Us Forest Service (FS); GRINDSTAFF, G. - University Of Oxford; Nichols, Mary

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/20/2023
Publication Date: 2/12/2024
Citation: Lapides, D., Grindstaff, G., Nichols, M.H. 2024. Automated earthwork detection using topological persistence. Water Resources Research. 650(2). Article e2023WR035990. https://doi.org/10.1029/2023WR035990.
DOI: https://doi.org/10.1029/2023WR035990

Interpretive Summary: The shape of landscapes controls how water moves over the surface. Humans have modified landscapes by building earthen structures to direct water for thousands of years. The legacy of human water management systems remains in many places around the world, and land managers need to understand how these structures impact hydrology and where they are in order to support management decisions. Earthen dams, berms, and stock ponds dot the southwestern United States. High-resolution elevation maps and imagery are increasingly available, and although man-made structures are readily visible to the eye, automatic detection remains a challenge. In this study, a method from the mathematical field of topology called ''persistence" was applied to automatically detect berms and stock ponds from elevation images. Results demonstrate that persistence is able to detect well-defined berms and stock ponds from targeted regions with 100% precision and accuracy. From a larger region, multiple criteria are required to detect structures while eliminating noise, resulting in precision of 67% and accuracy of 46%. Despite modest accuracy, these results are still promising since berms are generally found in groups. Thus, detection of even half of the berms substantially narrows down regions for manual inspection.

Technical Abstract: For thousands of years, humans have altered the movement of water through construction of earthworks. These earthworks remain in landscapes, where they continue to alter hydrology, even where structures have long since been abandoned. Management of lands containing earthworks requires an understanding of how the earthworks impact hydrology and knowledge of where the structures are located in the landscape. Various methods for detection of topographic features exist in the literature, including a set of rule and threshold-based techniques and machine learning methods. These tools are either labor-intensive or require special pre-processing or a priori assumptions about structures that limit generalizability. Here, we test a topological analysis tool called ''persistence" to determine if it is useful for earthwork detection in rangelands. We found that persistence can be used to detect earthworks with 100% precision and accuracy in targeted regions. In larger regions, multiple criteria are required to reduce topographic noise, and performance is reduced to 67% precision and 46% accuracy. Despite modest performance at larger spatial scales, these results indicate that persistence can be useful for terrain analysis, and it has the potential to substantially reduce manual effort in feature detection by identifying regions where berms may be found.