The Computational Archaeology Laboratory
The Computational Archaeology Laboratory at San Diego State University is a research and teaching facility focused on computational, spatial, and geoarchaeological approaches to understanding archaeological landscapes and long-term human-environment systems.
The lab brings together GIS and spatial analysis, computational modeling, scientific computing, digital field methods, 3D documentation, and geoarchaeology. A particular emphasis is placed on integrating archaeological fieldwork and empirical data with computational methods for investigating how people interacted with, modified, and moved through landscapes over long periods of time.
Lab Information
Location: Hardy Tower 62/66.
The lab is located on the ground floor of Hardy Tower. Students should enter through the HT 62 door in the stairwell and follow signs for the Computational Archaeology Laboratory.
Research
Research in the Computational Archaeology Laboratory examines archaeological problems through the integration of field archaeology and geoarchaeology, spatial and computational analysis, and environmental evidence.
| Research themes | Computational and analytical approaches |
|---|---|
| Human-landscape interactions and anthropogenic landscapes | GIS, spatial analysis, and geomorphometry |
| Long-term socio-environmental dynamics | Computational modeling and simulation |
| Agro-pastoral land use and subsistence | Agent-based modeling |
| Landscape evolution and geoarchaeology | Terrain and hydrological modeling |
| Archaeological mobility, territoriality, and interaction | Movement, accessibility, and network modeling |
| Archaeological survey and settlement patterns | Spatial statistics and predictive modeling |
| Digital archaeology and archaeological documentation | Remote sensing, photogrammetry, LiDAR, and 3D analysis |
Our research is geographically diverse, with ongoing and recent work in the Mediterranean, Southwest Asia, Central Eurasia, and Southern California.
Students and Research Opportunities
The Computational Archaeology Laboratory supports undergraduate and graduate research in computational archaeology, geoarchaeology, landscape archaeology, and related areas. Students use the lab for thesis research, independent studies, course projects, internships, and collaborative research. Opportunities for undergraduate research volunteers and internships for academic credit are regularly available, with occasional paid research positions depending on project funding. Please email me if you are interested.
Prospective Master’s Students
I welcome inquiries from prospective MA students interested in computational and digital archaeology, GIS and spatial analysis, landscape archaeology, historic preservation, archaeological survey, geoarchaeology, remote sensing and 3D documentation, or computational approaches to long-term human-environment interactions.
I am particularly interested in working with students who want to develop projects involving archaeological GIS and spatial modeling; computational modeling and simulation; landscape and settlement analysis; archaeological survey; remote sensing, photogrammetry/LiDAR or 3D documentation; geoarchaeology and anthropogenic landscapes; or reproducible computational research.
Potential thesis projects can draw on active field research, existing archaeological and spatial datasets, legacy survey collections, or independently developed research questions. Current and recent research provides opportunities to work with archaeological materials and data from Southern California, southern Italy, Jordan, Kazakhstan, Spain, and other regions.
Prospective students do not need to arrive with advanced computational expertise. Some previous experience with GIS, programming, quantitative methods, geoscience, or digital archaeological methods is useful, but a strong archaeological question and an interest in developing computational or geoarchaeological skills are equally important.
Interested prospective students should email me to discuss possible research directions. You are also encouraged to visit the main Anthropology MA program page for more information about graduate study at SDSU.
Courses
The lab provides specialized facilities for courses in computational archaeology, GIS, landscape archaeology, and geoarchaeology. It also serves as a workspace where students can continue course-based analyses using software and equipment not readily available elsewhere on campus.
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ANTH 560:Advanced Archaeological Field Techniques
Archaeological survey methods, non-invasive field recordation, digital documentation techniques, database design, 3D scanning, UAV documentation, architectural survey -
ANTH 562: Archaeological GIS and Geospatial Technologies
GIS, spatial analysis, terrain modeling, remote sensing, archaeological survey, spatial statistics, and digital archaeological workflows. -
ANTH 563: Anthropogenic Landscapes
Geoarchaeology, landscape evolution, sediment analysis, human-environment interactions, erosion and landscape modeling, and computational approaches to anthropogenic landscapes.
Laboratory Capabilities and Resources
The Computational Archaeology Laboratory combines high-performance computing with specialized archaeological, geoarchaeological, imaging, and field equipment. Rather than emphasizing particular hardware platforms, the laboratory is designed to support integrated workflows extending from archaeological field observation and data collection through laboratory analysis, spatial modeling, simulation, and visualization.
Geocomputation and Scientific Computing
The lab maintains high-performance Linux workstations for GIS, scientific computing, spatial modeling, simulation, image processing, photogrammetry, and analysis of large archaeological datasets. Dedicated high-performance computing resources support parallel and computationally intensive research.
The laboratory emphasizes reproducible computational workflows and Free and Open Source Software. Core areas of expertise include GRASS GIS and QGIS, Python and R, agent-based modeling, spatial statistics, image analysis, point-cloud and mesh processing, and shell-based scientific computing.
Digital Documentation and 3D Archaeology
The lab supports archaeological documentation at scales ranging from individual artifacts and architectural features to archaeological sites and landscapes. Available equipment supports close-range photogrammetry, structured-light scanning, terrestrial and mobile LiDAR, 360-degree imaging, calibrated photography, and multispectral imaging.
Geoarchaeology and Sediment Analysis
Laboratory and field resources support sediment characterization, particle-size and granulometric analysis, soil and sediment sampling, basic geochemical measurements, and archaeological sediment description. These facilities support research on anthropogenic sediments, landscape formation, erosion and deposition, and the long-term interactions between human activities and geomorphic processes.
Digital Archaeological Field Methods
The lab maintains equipment for digital archaeological survey and field recording, including GNSS positioning, mobile data collection, GIS-based field recording, photographic and 3D documentation, and portable computing. These resources support workflows that connect field observations directly with spatial databases and subsequent computational analysis. Our general mobile and digital field data collection workflow is described here.
Archaeological Analysis
Additional facilities support low-power digital microscopy, artifact measurement and morphometric analysis, microrefuse analysis, experimental archaeology, and general archaeological laboratory work.
Open and Reproducible Archaeological Computing
A central principle of the Computational Archaeology Laboratory is that sophisticated computational archaeology should not depend upon inaccessible or proprietary technological ecosystems. Research and teaching therefore emphasize Free and Open Source Software, transparent analytical procedures, reproducible workflows, and methods that students and collaborators can continue to use outside the laboratory.
This approach extends across GIS and spatial analysis, programming and data science, simulation, image analysis, photogrammetry, 3D processing, and field data collection. The lab has particular expertise in GRASS GIS, QGIS, Python, R, NetLogo, OpenDroneMap, CloudCompare, MeshLab, and related open-source tools.