This project will develop and operationalize a standardized cultural resource detection protocol and computational model integrating remote sensing (RS) technologies—aerial lidar, optical, thermal, and geophysical survey data—to enable rapid, non-invasive identification and assessment of cultural resources on Department of War (DoW) installation lands. Current survey methods produce inconsistent, unreliable datasets that often miss well-preserved archaeological features. By systematically evaluating multi-sensor RS integration, acquisition parameters, and computational feature extraction methods, this work will overcome the limitations of conventional surveys and establish a transferable framework for DoW installations across varied terrain and vegetation conditions.

This project will demonstrate the mechanisms, costs, and benefits of applying systematic and standardized RS procedures in cultural resource management to support mission readiness, environmental stewardship, and landscape-level planning and resource management. The technology demonstrated by this project will capture optical, thermal, lidar, and geophysical survey data with a single, blue-listed small unmanned aerial system and also with ground-based geophysical survey methods. RS surveys will be conducted at varying flight speeds, altitudes, sampling distances, and overlap widths to assess the costs and benefits of different acquisition strategies and survey intensities. Scheduling of RS surveys will take into account meteorological conditions, seasonality, vegetation cover, and diurnal temperature variation. RS surveys will take place at Florence Military Reservation in Arizona. Previously acquired conventional survey data will serve as baseline data for assessing the efficacy and cost-effectiveness of RS survey technologies.

Processing of RS datasets will follow a three-tiered approach. 1) RS datasets will be used along with baseline conventional survey data to define the signature profiles of various anthropogenic activities, archaeological features, and cultural deposits. Applying a feature-based manual (brute-force) approach, data processing will follow established procedures to identify their spectral, sedimentary, biological, morphometric, topographic, and magnetic signatures. The effect of various survey parameters and conditions on the capability of individual RS technologies to detect these signatures will also be evaluated. 2) Automated feature-based approaches—object-based image analysis and neural network models—will be applied for increased efficiency. 3) To achieve true data integration, the project team will develop and deploy a site-based approach establishing the correlation between archaeological sites and spatial variability of multisensory datasets (Local Variance Model). The cost-effectiveness of RS surveys will be assessed in comparison to conventional survey in terms of data collection, personnel, expertise, and data storage and management. The geographic limitations and transferability of the developed approach will be evaluated through a range ongoing or planned but independently funded research projects in collaboration with multiple universities.

This research supports DoW's need to efficiently manage cultural resources while maintaining operational access to training lands. DoW requires complete project tracking for regulatory actions, advanced survey and data recovery technologies to speed up cultural resource surveys, and efficiencies that allow for streamlined review. The successful demonstration of this project will accelerate assessment timelines, reduce the personnel and expertise burden on installation cultural resource staff, and enable landscape-level resource management that focuses effort on sites with genuine significance and preservation potential. The framework will be applicable across DoW's 568,000 assets at nearly 4,800 sites worldwide, with particular utility for arid and semi-arid installations. (Anticipated Project Completion - 2027)