Our Research

1. *Lv, Z.†, Nunez, K., Brewer, E.†, Runfola, D. 2023. pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2022.105296

2. *Brewer, E.†, Lin, J.†, Runfola, D. 2022. Susceptibility & defense of satellite image-trained convolutional networks to backdoor attacks. Information Sciences. https://doi.org/10.1016/j.ins.2022.05.004

3. Runfola, D., Baier, H.†, Mills, L.†, Naughton-Rockwell, M.†, Stefanidis, A. 2022. Deep Learning Fusion of Satellite and Social Information to Estimate Human Migratory Flows. Transactions in GIS. http://doi.org/10.1111/tgis.12953

4. *Karim, B.†, Runfola, D. 2021. Toponym-assisted map georeferencing: Evaluating the use of toponyms for the digitization of map collections. PLoS One. https://doi.org/10.1371/journal.pone.0260039

5. Runfola, D., Stefanidis, A., Baier, H.†, 2021. Using Satellite Data and Deep Learning to Estimate Educational Outcomes in Data Sparse Environments. Remote Sensing Letters 13(1). https://doi.org/10.1080/2150704X.2021.1987575

6. *Brewer, E.†, Kemper, P., Lin, J.†, Hennin, J.†, and Runfola, D. 2021. Predicting Road Quality using High Resolution Satellite Imagery: A Transfer Learning Approach. PLoS One. https://doi.org/10.1371/journal.pone.0253370

7. *Goodman, S.†, BenYishay, A., Runfola, D. 2020. A Convolutional Neural Network Approach to Predict Non Permissive Environments from Moderate Resolution Imagery. Transactions in GIS. https://doi.org/10.1111/tgis.12661

8. Runfola D., Anderson A†, Baier H†, Crittenden M†, Dowker E†, Fuhrig S†, et al.† (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. https://doi.org/10.1371/journal.pone.0231866

9. Runfola, D.; Batra, G.; Anand, A.; Way, A.†; Goodman, S.† 2020. Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach. Sustainability, 12, 3225. https://doi.org/10.3390/su12083225

10. *Goodman, S.†, BenYishay, A., Lv, Z.†, Runfola, D., 2019. GeoQuery: Integrating HPC systems and public web-based geospatial data tools. Computers and Geosciences. https://doi.org/10.1016/j.cageo.2018.10.009

11. *Marty, R.†, Goodman, S.†, LeFew, M.†, Dolan, C., BenYishay, A., Runfola, D. 2019. Assessing the Causal Impact of Chinese Aid on Vegetative Land Cover in Burundi and Rwanda Under Conditions of Spatial Imprecision. Development Engineering. https://doi.org/10.1016/j.deveng.2018.11.001

12. Buchanan, G., Parks, B., Donald, P., O’Donnel, B., Runfola, D., Swaddle, J., Tracewski, L., Butchart, S. 2018. The Local Impacts of World Bank Development Projects Near Sites of Conservation Significance. Journal of Environment and Development. https://doi.org/10.1177/1070496518785943

13. Bunte, J., Desai, H.†, Gbala, K., Parks, B., Runfola, D.M., 2018. Natural resource sector FDI, government policy, and economic growth: Quasi-experimental evidence from Liberia. World Development. Volume 107. pg 151-162. https://doi.org/10.1016/j.worlddev.2018.02.034.

14. Hughes, S., Cormier, B., Runfola, D.M., 2018. Issue Proximity and Policy Response in Local Governments. Review of Policy Research. https://doi.org/10.1111/ropr.12285

15. BenYishay, A., Heuser, S., Runfola, D.M., Trichler, R. 2017. Indigenous land rights and deforestation: Evidence from the Brazilian Amazon. Journal of Environmental Economics and Management. https://doi.org/10.1016/j.jeem.2017.07.008

16. Runfola, D., Ariel BenYishay, Jeffery Tanner, Graeme Buchanan, Jyoteshwar Nagol, Matthias Leu, Seth Goodman†, Rachel Trichler and Robert Marty†. 2017. A Top-Down Approach to Estimating Spatially Heterogeneous Impacts of Development Aid on Vegetative Carbon Sequestration. Sustainability 9(3), 409. doi:10.3390/su9030409. https://doi.org/10.3390/su9030409.

17. Runfola, D.M., Samuel Ratick, Julie Blue, Eia Axnia Machado, Nupur Hiremath, Nick Giner, Kathleen White (USACE), Jeffrey Arnold (USACE), 2017. “A Multi-Criteria Geographic Information Systems Approach for the Measurement of Vulnerability to Climate Change.” Mitigation and Adaptation Strategies for Global Change. https://doi.org/10.1007/s11027-015-9674-8

18. Marty, R.†, Dolan, C., Leu, M., Runfola, D. 2017. Taking the Health Aid Debate to the Sub-National Level: The Impact and Allocation of Foreign Health Aid in Malawi. BMJ Global Health. DOI: 10.1136/bmjgh-2016-000129.

19. Nawrotzki, R. J., Runfola, D. M., Hunter, L. M., and Riosmena, F. 2016. Domestic and international climate migration from rural Mexico. Human Ecology 44(6), 687-699. DOI: 10.1007/s10745-016-9859-0

20. Leyk, S., Runfola, D.M., Riosmena, F. ,Hunter, L. and Nawrotzki, R., “Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data.” 2016. Population, Space and Place. 23(6), e2047

21. Runfola, D.M., Napier, A., 2016. “Migration, climate, and international aid: examining evidence of satellite, aid, and micro-census data.” Migration and Development. 5.2 (2016): 275-292.

22. Kevin Robert Gurney, Paty Romero-Lankao, Karen C. Seto, Lucy R. Hutyra, Riley Duren, Christopher Kennedy, Nancy B. Grimm, James R. Ehleringer, Peter Marcotullio, Sarah Hughes, Stephanie Pincetl, Mikhail V. Chester, Daniel M. Runfola, Johannes J. Feddema, Joshua Sperling. 2015. “Climate change: Track urban emissions on a human scale.” 2015. Nature (Comment). 525, 179-181.  doi:10.1038/525179a

23. Nawrotzki, R. J., Hunter, L. M., Runfola, D. M., Riosmena, F. (2015). Climate change as migration driver from rural and urban Mexico. Environmental Research Letters 10(11), 114023. DOI: 10.1088/1748-9326/10/11/114023. 

24. Nawrotzki, R. J., Riosmena, F., Hunter, L. M., Runfola, D. M. (2015). Undocumented migration in response to climate change. International Journal of Population Studies 1(1), 60-74. DOI: 10.18063/IJPS.2015.01.004. 

25. Nawrotzki, R. J., Riosmena, F., Hunter, L. M., & Runfola, D. M, 2015. Amplification or suppression: Social networks and the climate change – migration association in rural Mexico. Global Environmental Change 35, 463-474. DOI: 10.1016/j.gloenvcha.2015.09.002

26. Runfola, D.M., Romero-Lankao, P., Leiwen, J., Hunter, L., Nawrotzki, R., and Sanchez, L., 2015. “The Influence of Migration on Exposure to Extreme Weather Events: A Case Study in Mexico.” 2015, Society and Natural Resources. https://doi.org/10.1080/08941920.2015.1076918

27. Runfola, D.M., Hughes, S., 2014. “What makes green cities unique? Examining the economic and political characteristics of grey and green cities.” Land, 3(1), 131-147; doi:10.3390/land3010131.

28. Marcotullio, Peter J., Sara Hughes, Andrea Sarzynski, Stephanie Pincetl, Landy Sanchez Peña, Patricia Romero‐Lankao, Daniel Runfola, and Karen C. Seto,  2014. "Urbanization and the carbon cycle: Contributions from social science." Earth's Future (2) 596-514. https://doi.org/10.1002/2014EF000257

29. Giner, N.M., Polsky, C., Pontius, Jr., R.G., Runfola, D.M., Ratick, S., 2014. “Creating spatially-explicit lawn maps without classifying remotely-sensed imagery: The case of suburban Boston, Massachusetts, USA.” Cities and the Environment (CATE), 7(1), 10.

30. Patricia Romero-Lankao, Kevin Gurney, Karen Seto, Mikhail Chester, Riley M. Duren, Sara Hughes, Lucy R. Hutyra, Peter Marcotullio, Larry Baker, Nancy B. Grimm, Chris Kennedy, Elisabeth Larson, Stephanie Pincetl, Dan Runfola, Landy Sanchez, Gyami Shrestha, Johannes Feddema, Andrea Sarzynski, Joshua Sperling, and Eleanor Stokes, 2014. “A critical knowledge pathway to low-carbon, sustainable futures: integrated understanding of urbanization, urban areas and carbon.” Earth’s Future 2(10) pp. 515-532.

31. Runfola, D.M., Pontius Jr., R.G., 2013. "Quantifying the temporal instability of land change transitions." International Journal of GIS, 27(9), 1696-1716.

32. Runfola, D.M., Polsky, C., Nicolson, C., Giner, N., Pontius Jr., R.G., Krahe, J., Decatur, A., 2013. "A Growing Concern? Examining the Influence of Lawn Size on Residential Water Use in Suburban Boston, MA, USA" Landscape and Urban Planning, 119, 112-123.

33. Runfola, D.M., Hamill, T., Pontius, R.G., Rogan, J., Polsky, C., Albert, D., Ratick, S., 2014. “Using Fine Resolution Orthoimagery and Spatial Interpolation to Rapidly Map Turf Grass in Suburban Massachusetts.”  International Journal of Geospatial and Environmental Research.  1:4. https://dc.uwm.edu/ijger/vol1/iss1/4.

34. Giner, N.M., Polsky, C., Pontius Jr., R.G., and Runfola, D.M., 2013. "Understanding the determinants of lawn landscapes: A fine-resolution spatial statistical analysis in suburban Boston, Massachusetts, USA." Landscape and Urban Planning, 111, 25-33.

35. Islam, N., Kitazawar, D., Runfola, D.M., Giner, N., 2012. "Urban Lakes in a Developing Nation: Drivers, States, and Impacts of Water Quality and Quantity in Dhaka, Bangladesh." Lakes & Reservoirs: Research and Management. 17(4): 253-263.

36. Gao, Y., Marpu, P., Niemeyer, I., Runfola, D.M., Giner, N., Hamill, T., Pontius, G.R. Jr., 2011. "Object-based classification with features extracted by a semi-automatic feature extraction algorithm - SEaTH." Geocarto International, 26 (3).

37. Rogan, J., Bumbarger, N., Kulakowski, D., Christman, Z., Runfola, D.M., Blanchard, S., 2011. "Improving forest type discrimination with mixed lifeform classes using fuzzy classification thresholds informed by field observations." Canadian Journal of Remote Sensing, 36 (6).

38. Griffin, S., Rogan, J., Runfola, D.M., 2011. "Application of Spectral and Environmental Variables to Map the Kissimmee Prairie Ecosystem using Classification Trees." GIScience & Remote Sensing, 48(3).

39. Fortier, J., Rogan, J., Woodcock, C., Runfola, D.M., 2011. "Utilizing temporally invariant calibration sites to classify multiple dates of satellite imagery." Photogrammetric Engineering & Remote Sensing, 77 (2): p.181.

40. Runfola, D.M., and Katherine B. Hankins. 2010. "Urban dereliction as environmental injustice." ACME: An International Journal for Critical Geographies 9(3): 345-367

The geoLab has partnered with a wide range of external partners to enable our students to gain real-world experience while still at William & Mary.  To date, these partnerships have included:

Tearline.mil (National Geospatial Intelligence Agency). GeoLab is a long-term partner of NGA's Tearline Program. Since 2020, GeoLab has published Tearline articles on topics such as China's Belt Road investments globally, environmental impacts in Ukraine, and Russian influence in Africa. GeoLab also provides joint project support to newer Tearline partners so they may benefit from GeoLab's institutional knowledge with NGA's Tearline Program. 

GRID3. Students have worked with the GRID3 program to help identify and delineate healthcare catchment areas on the African continent.  We continue to work closely with GRID3 in order to facilitate the identification of administrative boundaries in similar locations.

Commonwealth Cyber Initiative (CCI). Students have worked with external practitioners from a wide range of federal and other agencies to explore topics at the nexus of cybersecurity and satellite imagery.

Nuru International. Students are working to analyze satellite imagery of intervention areas where Nuru International has worked with local practitioners to improve agricultural productivity.

DoD Joint Personnel Recovery Agency (JPRA). Projects with the JPRA have focused on building web and local apps that will aid combatants trapped behind enemy lines in identifying safe environments in which they can wait for rescue.

United States Army Training and Doctrine Command (TRADOC). Projects with the JPRA have focused on building web and local apps that will aid combatants trapped behind enemy lines in identifying safe environments in which they can wait for rescue.

Global Environment Facility (GEF). This project focuses on better understanding the efficacy of climate related activities implemented by the Global Environment Facility across the entire world.  Students participate by identifying the locations of interventions, metadata about these interventions, and modeling the effects of these interventions using python and R-based code.

United States Department of Homeland Security (DHS). This project focuses on implementing deep learning models to better understand and predict migratory flows to the southern border of the United States.  Students are expected to collect census information, implement existing models, and detail findings of their analyses in public forums.