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Giorgos Mountrakis, Ph.D.
Founder & Director | Intelligent Geocomputing Laboratory
Professor | Dept. of Environmental Resources Engineering | State University of NY College of Environmental Science and Forestry
Associate Editor | ISPRS Journal of Photogrammetry and Remote Sensing

Giorgos Mountrakis
Research Expertise

Remote Sensing Methods: Deep learning for classification/regression, three-dimensional LiDAR processing, accuracy assessment, image fusion.

Interdisciplinary Studies: Land cover dynamics, socioeconomic patterns, environmental covariates, climate pattern analysis, forest consolidation dynamics, biodiversity responses, urban growth modeling.

CV

Full CV for Dr. Mountrakis.

Short Biography

Dr. Giorgos Mountrakis is a Professor in Environmental Resources Engineering at the State University of New York College of Environmental Science and Forestry. He holds a Ph.D. from the University of Maine in Spatial Information Engineering and Science (2004). He is the Founder of the Intelligent Geocomputing Laboratory at SUNY-ESF. His areas of expertise include:

i) environmental monitoring using remote sensing methods (deep learning for classification/regression, three-dimensional LiDAR processing, accuracy assessment, image fusion),

ii) interdisciplinary studies linking land cover dynamics, socioeconomic patterns and environmental covariates (climate pattern analysis, forest consolidation dynamics, biodiversity responses to climate/land cover changes, urban growth modeling).

Dr. Mountrakis has been successful at securing external competitive grants from NASA, NSF, USDA Forest Service, USAID and Syracuse Center of Excellence. He has been the lead PI for approximately $3.4M in research funds, through individual and collaborative grants ($3.8M including Co-PI status). He has published in numerous journals and books and has presented his work in various national and international conferences. He is the recipient of several awards including SUNY ESF's Exemplary Researcher Award (2015), NASA's New Investigator Award (2008), ISPRS Excellent Reviewer Award (2012) and a Postdoctoral Fellowship from the National Academy of Sciences (2004).

His teaching is innovative through student-engaging activities (e.g. incorporating inquiry-based learning) and it includes courses in Digital Image Analysis, Spatial Statistics, Remote Sensing, Surveying, GPS and Artificial Intelligence in Geography. Notable service activities include being an Associate Editor for the ISPRS journal, chairing an ASPRS National Committee on Academic Engagement, Guest Editorship in the October 2008 Special Issue on "Artificial Intelligence in Remote Sensing" for the Photogrammetric Eng. & Remote Sensing Journal, and his involvement in a United Nations FAO Thematic Study on Trees Outside the Forest. In 2012 he was a Keynote Speaker at the 32nd EARSEL Symposium.

Awards & Recognition

• SUNY ESF's Exemplary Researcher Award (2015).

• NASA's New Investigator Award (2008).

• ISPRS Excellent Reviewer Award (2012).

• Postdoctoral Fellowship from the National Academy of Sciences (2004).

Publications

Google Scholar Profile, > 8000 citations.

+ identifies current or former graduate student.

Patent

[P01] G. Mountrakis (Inventor). Multi-scale radial basis function neural network. Full patent (#7,577,626) Issued August 18, 2009.

Review & Meta-Analysis Studies

[63] Wang+, Z., G. Mountrakis and N. Pastick (2025). Deep learning approaches on Landsat observations: a review and meta-analysis. International Journal of Remote Sensing, 46(15), 5601–5627. [pdf]

[61] Wang+, Z. and G. Mountrakis (2023). Accuracy Assessment of Eleven Medium Resolution Global and Regional Land Cover Land Use Products. Remote Sensing 15 (12), 3186. [pdf]

[56] Khan, R.M.; Salehi, B.; et al. (2021). A Meta-Analysis on Harmful Algal Bloom Detection and Monitoring. Remote Sensing, 13, 4347. [pdf]

[52] Mountrakis, G., Li, J., Lu, X., Hellwich, O. (2018). Deep learning for remotely sensed data. ISPRS Journal, 145, 1-2. [pdf]

[50] Heydari+ S., G. Mountrakis (2019). A meta-analysis of deep neural networks in remote sensing. ISPRS Journal, 152, 192-210. [pdf]

[39] R. Khatami+, G. Mountrakis, S. Stehman (2016). A meta-analysis of remote sensing research on land-cover image classification. Remote Sensing of Environment, 177, 89–100. [pdf]

[34] G. Grekousis+, G. Mountrakis, M. Kavouras (2015). An overview of 21 global and 43 regional land cover mapping products. International Journal of Remote Sensing, 36(21), 5309-5335. [pdf]

[22] D. Triantakonstantis+, G. Mountrakis (2012). Urban growth prediction: A review of computational models. Journal of Geographic System, 4(6): 555-587. [pdf]

[10] K. Gunson+, G. Mountrakis, L. Quackenbush (2011). Spatial wildlife-vehicle collision models: A review. Journal of Environmental Management, 92(4):1074-1082. [pdf]

[09] G. Mountrakis, J. Im, C. Ogole+ (2011). Support vector machines in remote sensing: A review. ISPRS Journal, 66(3):247-259. [pdf]

Accuracy Assessment Studies

[46] R. Khatami+, G. Mountrakis, S. Stehman (2017). Predicting individual pixel error in remote sensing soft classification. Remote Sensing of Environment, 199, 401-414. [pdf]

[41] R. Khatami+, G. Mountrakis, S. Stehman (2017). Mapping per-pixel predicted accuracy of classified remote sensing images. Remote Sensing of Environment, 191, 156–167. [pdf]

[25] G. Mountrakis, B. Xi+ (2013). Assessing Reference Dataset Representativeness through Confidence Metrics. ISPRS Journal, 78:129-147. [pdf]

Optical Sensor Studies

[62] Mountrakis, G. and Heydari+, S. (2024). Effect of intra-year Landsat scene availability in land cover classification using deep neural networks. ISPRS Journal, 212, 164-180. [pdf]

[60] Mountrakis, G. and Heydari+, S. (2023). Harvesting the Landsat archive for land cover classification using deep neural networks. ISPRS Journal, 200, 106-119. [pdf]

[59] Pede, T.+, G. Mountrakis (2022). Towards daily maximum heat index estimation using satellite-derived products. International Journal of Remote Sensing, 43 (8), 2861-2884. [pdf]

[49] Pede+, T., G. Mountrakis (2018). An empirical comparison of interpolation methods for MODIS LST composites. ISPRS Journal, 142, 137-150. [pdf]

[48] Khatami+ R., G. Mountrakis (2018). The interacting effects of image parameters on impervious cover classification. Remote Sensing Letters, 9(2): 189-198. [pdf]

[47] S. Heydari+, G. Mountrakis (2018). Effect of classifier selection on per-pixel classification accuracy using 26 Landsat sites. RSE, 204, 648-658. [pdf]

[44] M.Y. Paltsyn, J. Gibbs, G. Mountrakis (2017). Estimation of Grassland Cover using MODIS. Rangeland Ecology, 70 (6), 723-729. [link]

[40] Z. Xu+, G. Mountrakis, L.J. Quackenbush (2017). Impervious surface extraction in imbalanced datasets. IJRS, 38(1), 43-63. [pdf]

[35] I. Manakos, E. Technitou, G. Mountrakis, et al. (2016). Multi-modal knowledge base for habitat mapping. European Journal of RS, 49, 1033-1060. [pdf]

[33] S. Yousefi, R. Khatami+, G. Mountrakis, et al. (2015). Accuracy assessment of classifiers in Iran. EMA, 187:641. [pdf]

[26] H. Jin+, S. Stehman, G. Mountrakis (2014). Impact of training sample selection on urban classification. IJRS, 35(6): 2067-2081. [pdf]

[20] H. Jin+, G. Mountrakis, P. Li (2012). Super-resolution mapping using variograms. IJRS, 33(24): 7747–7773. [pdf]

[17] L. Luo+, G. Mountrakis (2012). Multi-process model for impervious surface detection. IJRS, 33(2):365-381. [pdf]

[14] L. Luo+, G. Mountrakis (2011). Converting spectral information for impervious surface classification. ISPRS Journal, 66(5): 579–587. [pdf]

[12] G. Mountrakis, L. Luo+ (2011). Enhancing spectral information with structural inputs. RSE, 115(5):1162-1170. [pdf]

[11] B. Gong, J. Im, G. Mountrakis (2011). Artificial Immune Network for Land Use Classification. RSE, 115(2):600-614. [link]

[08] L. Luo+, G. Mountrakis (2010). Integrating intermediate inputs in hybrid classification. RSE, 114(6):1220-1229. [pdf]

[06] G. Mountrakis, R. Watts, L. Luo+, J. Wang+ (2009). Developing Collaborative Classifiers. PE&RS, 75(7):831-844. [pdf]

Radar Sensor Studies

[30] H. Jin+, G. Mountrakis, S. Stehman (2014). Assessing PALSAR metrics in land cover classification. ISPRS Journal, 98:70–84. [pdf]

LiDAR Studies

[45] G. Mountrakis, Y. Lee+ (2017). Gaussian Decomposition for waveform LiDAR. ISPRS Journal, 129, 200-211. [pdf]

[31] W. Zhuang+, G. Mountrakis, J. Wiley, C. Beier (2015). Forest Biomass Using Waveform Lidar. IJRS, 36(7), 1871-1889. [pdf]

[28] W. Zhuang+, G. Mountrakis (2014). Ground peak identification for waveform LiDAR. ISPRS Journal, 95:81-92. [pdf]

Sensor Fusion Studies

[58] H. Jin+, G. Mountrakis (2022). Fusion of optical, radar and LiDAR for land cover classification. ISPRS Journal, 187, 171-190. [pdf]

[38] A. Endres+, G. Mountrakis, et al. (2016). Importance of Landsat, LIDAR and PALSAR for biomass estimation. European Journal of RS, 49, 795-807. [pdf]

[29] W. Zhuang+, G. Mountrakis (2014). Ground peak identification using LiDAR and Landsat. International Journal of Digital Earth, 1-44. [pdf]

[24] H. Jin+, G. Mountrakis (2013). Integration of urban growth modeling and image analysis. IJRS, 34(15): 5468-5486. [pdf]

Interdisciplinary Studies
Forest/Vegetation

[57]Mountrakis G, Yang S.+ (2021). Linking Population and Forest Dynamics. Advances in Environmental Research, 2(1):8. [pdf]

[55]Mountrakis G, Yang S.+ (2021). Contributing Factors to Forest Loss. Advances in Environmental Research, 2(4):17. [pdf]

[53] Paltsyn, M.Y., J. Gibbs, G. Mountrakis (2019). Traditional Knowledge and Rangeland Monitoring. Environmental Management, 64 (1), 40-51. [pdf]

[51] Iegorova. L.V., J. Gibbs, G. Mountrakis, et al. (2019). Rangeland vegetation dynamics in Altai. Environmental Research Letters. [pdf]

[42] S. Yang+, G. Mountrakis (2017). Forest dynamics in the U.S. PLoS ONE 12(2): e0171383. [pdf]

[37] K. Pandit, E. Bevilacqua, G. Mountrakis (2016). Spatial Analysis of Forest Crimes. Journal of Geospatial Applications, 1(1), 3. [pdf]

[27] L.M. Giencke, M.Dovciak, G. Mountrakis, et al. (2014). Beech bark disease spatial patterns. Canadian Journal of Forest Research, 44:1042–1050. [pdf]

Urban

[32] G. Grekousis+, G. Mountrakis (2015). Sustainable development under population pressure. PLoS ONE 10(3): e0119675. [pdf]

[15] D. Triantakonstantis+, G. Mountrakis, J. Wang+ (2011). Spatially Heterogeneous Urban Growth Model. JGIS, 3(3):195-210. [pdf]

[13] J. Wang+, G. Mountrakis (2011). Multi-network urbanization model. IJGIS, 25(2):229-253. [pdf]

[07] G. Mountrakis, K. Gunson+ (2009). Moose-vehicle collisions spatiotemporal analysis. IJGIS, 23(11):1389-1412. [pdf]

Agriculture

[54] Pede, T.+, G. Mountrakis, S. Shaw (2019). Corn yield prediction using MODIS LST. Agricultural and Forest Meteorology, 276, 10761. [pdf]

[36] G. Grekousis+, G. Mountrakis, M. Kavouras (2016). Linking forest/cropland to socioeconomic indicators for EU. GIScience and RS, 53(1), 122-146. [pdf]

Water/Ice

[43] M.A. Petrov, et al., G. Mountrakis (2017). Glacial lake inventory in Uzbekistan. Science of Total Environment, 592, 228-242. [link]

[23] R. Khatami+, G. Mountrakis (2012). Classification in Hurricane Katrina Flooding Analysis. Remote Sensing, 4(12), 3877-3891. [pdf]

[18] B. Hong, K. Limburg, M. Hall, G. Mountrakis, et al. (2012). Monitoring framework for urbanizing watersheds. Environmental Modelling & Software, 32:1-15. [pdf]

Algorithms/Databases

[21] G. Mountrakis, W. Zhuang+ (2012). Multi-Scale RBF Network Training. PLOS One, 7(8): e40093. [pdf], [link]

[16] G. Mountrakis, A. Stefanidis (2011). Personalized Geospatial Queries. JGIS, 3(4): 334-344. [pdf]

[03] G. Mountrakis, P. Agouris, A. Stefanidis (2005). Adaptable User Profiles. Transactions in GIS, 9 (4), 561-583. [pdf]

[02] G. Mountrakis, P. Agouris, I. Schlaisich, A. Stefanidis (2004). Quality-Based Image Retrieval. PE&RS, 70 (8), 973-981. [pdf]

[01] P. Agouris, K. Beard, G. Mountrakis, A. Stefanidis (2000). Spatio-Temporal Gazetteer Framework. PE&RS, 66 (10), 1224-1250. [pdf]

Other

[05] G. Mountrakis, A. Stefanidis (2008, not refereed). Foreword: AI in Remote Sensing. PE&RS, 74(10):1199. [link]

[04] G. Mountrakis (2008, not refereed). Next generation classifiers. PE&RS, 74(10):1178-1180. [pdf]

Educational Research

[19] G. Mountrakis, D. Triantakonstantis+ (2012). Space balloon educational experiment. Journal of Geography in Higher Education, 36(3): 385-401. [pdf]

Grants

Cumulative award amount as lead PI or Co-PI: $3.8M, as lead PI: $3.4M.

Current Grants
  • "A decision-making activity to guide archipelago-wide rewilding of Galapagos giant tortoises"
    NASA Ecological Forecasting. Lead PI with James Gibbs. $797,000. 2021-2025.
  • "Developing novel deep learning classifiers for remote sensing imagery"
    SRC Inc. Lead PI. $150,000. 2021-2025.
Past Grants
  • "Spearheading new economic development while protecting tourism and access to potable water: Satellite-based harmful algal bloom detection in Onondaga County"
    Onondaga County Economic Development. Lead PI. $100,000. 2019-2021.
  • "Developing advanced scientific capabilities and new economic opportunities from harmful algal bloom detection using remotely-sensed imagery"
    Healthy Water Solutions Center of Excellence. PI with Tyler Smith. $10,000. 2020-2021.
  • "Management of Social–Ecological Grazing Systems in the Altai Mountain Transboundary Zone"
    NASA. PI with James Gibbs. $779,000. 2015-2018.
  • "Developing advanced accuracy metrics for satellite-derived forestry products"
    USDA Forest Service. PI with Steve Stehman. $128,386. 2014-2016.
  • "Engaging scientists and local herders as research collaborators to address grazing, poaching, and climate change issues in the Altai Mountain Region of Russia"
    USAID. Co-PI with James Gibbs, Jennifer Castner, Mikhail Paltsyn. $99,655. 2012-2014.
  • "Using LIDAR to assess the roles of climate and land-cover dynamics as drivers of change in biodiversity"
    NASA. PI with Bill Porter, Colin Beier, Lianjun Zhang, Ben Zuckerberg, Bryan Blair. $809,000. 2009-2013.
  • "Satellite-derived anthropogenic land use/land cover changes: Integrating detection, modeling and educational approaches"
    NASA. PI. $359,341. 2008-2012.
  • "Establishing a Novel Forest Assessment Method: The Forestless Volume Indicator"
    USDA Forest Service. PI. $120,000. 2008-2010.
  • "Bridging the temporal mismatch between remotely-sensed land use changes and field-based water quality/quantity observations"
    Syracuse Center of Excellence. PI with Karin Limburg, Myrna Hall, Bongghi Hong. $100,000. 2008-2009.
  • "Incorporating Spatially-Explicit Uncertainty Metrics in Image-Derived Classification of Impervious Surfaces"
    NSF. PI. $50,000. 2007-2008.
  • "An Integrated Monitoring/Modeling Framework for Assessing Human-Nature Interactions in Urbanizing Watersheds: Wappinger and Onondaga Creek Watersheds"
    Syracuse Center of Excellence. Co-PI with Karin Limburg, Myrna Hall, Bongghi Hong, Peter Groffman. $300,000. 2006-2008.
  • "Monitoring Human-Induced Land Use Changes along the Great Lakes"
    Great Lakes Research Consortium. PI. $10,000. 2006-2007.
  • "Synergetic Use of Satellite Imagery and Ancillary Data for Impervious Surface Estimation in the contiguous US"
    National Academy of Sciences and USGS. PI. $80,000. 2004-2005.

Our Lab

Dr. Mountrakis is the Founder of the Intelligent Geocomputing Laboratory in room 105 Baker.

Facilities & Equipment

• Eleven high-end workstations with 10-core CPUs.

• 64-core computational cluster.

• Access to NASA's and Syracuse University's supercomputing resources.

• Software: ArcGIS, Idrisi, Erdas Imagine, ENVI, Matlab, Python, Tensorflow.

Lab
Current Research Group

Atef Amriche - Ph.D.: Training dataset selection.

Babak Asadollah - Ph.D.: Heat Index estimation.

Ahmadreza Safaeinia - Ph.D.: Deep learning on Landsat.

Zhixin Wang - Ph.D.: DNN computational reduction.

Teaching

Courses designed for variety of backgrounds. Classes typically have a large project.

Current Courses

Fall 2025

  • Spatial Analysis (3cr) ERE621 Syllabus
    Topics: spatial statistics, modeling for various data formats. Methods include spatial randomness, Ripley's K, variograms, kriging. Matlab required. Programming experience needed.

Spring 2026

  • Principles of Remote Sensing (4cr) ERE365/ERE565. Syllabus
    Introduction to fundamentals of acquiring, analyzing and utilizing remote sensing data. No prior experience necessary.
  • Environmental Machine Learning (3cr) ERE596, section 3. Syllabus
    Introduction to machine learning and applications in environmental problems.
Past Courses
  • Digital Image Analysis (3cr) ERE622. Syllabus
    Digital image processing: enhancement, filtering, edge detection, morphology, recognition. Matlab assignments.
  • Surveying for Engineers (4cr) ERE371. Syllabus
    Surveying principles and practices for engineering and construction management.
  • Introduction to GPS (1cr) ERE566. Syllabus
    Theory and practice of GPS measurements.
  • Introduction to Spatial Information (1cr) ERE553. Syllabus
    Basic spatial terminology, position methods, accuracy and precision.
  • ESF Goes to Space (1cr) ERE496.
    Design, build, launch and retrieve high altitude sensor.
  • AI in Geography (1cr) ERE796.
    AI methods: decision trees, fuzzy logic, neural networks, genetic algorithms. Graduate students only.

Service

Includes highlights of academic and professional service activities.

Departmental

Chair of Review Committee on Promotion and Tenure.

College

Committee on Scholarship and Research.

Chair of Research Advisory Council.

Editorial

Associate Editor for ISPRS Journal.

Guest Editor for AI in Remote Sensing (2008).

Chair, ASPRS National Committee.

Open Positions

Our lab receives many requests, we only have time to respond to dedicated students submitting all requested material - see expression of interest email instructions below.

Available Openings

Several research and teaching assistantships available starting August 2026, funded by NASA, ESF and others. ML experience and strong programming required.

Our lab has hosted numerous Fulbright students - email me directly to investigate collaboration.

Expected qualifications:

— Master's degree in engineering, computer science, physics, geography or related disciplines.
— Strong statistical and programming background.
— Experience with machine learning methods.
— Excellent English verbal and writing skills.
— Ability to collaborate and lead in groups.

Expression of Interest Email

Important: Emails without the two-page info sheet or GRE scores will not be considered.

  1. Edit this document, max 2 pages.
  2. Combine two-page pdf with CV into single pdf.
  3. Name: FirstName_Lastname_ESF.pdf
  4. Email to: [email protected]
  5. You will receive feedback within one week.

GRE Waiver: Available if you have 3+ English journal publications as primary author.

Participating Graduate Programs

Environmental Resource Engineering – Geospatial Information Science and Engineering.

Environmental Science – Climate and Energy.

Environmental Science – Ecosystems: Land, Water and Air.

Media

Includes media coverage at national scale, not local/internal outlets.

Original Paper

Forest attrition study: S. Yang, G. Mountrakis (2017). Forest dynamics in the U.S. indicate disproportionate attrition in western forests, rural areas and public lands. PLoS ONE 12(2): e0171383.

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Media Coverage
╔═══════════════════╗ ║ SYSTEM STATUS ║ ╠═══════════════════╣ ║ CPU: ▓▓▓▓▓▓▓▓░░ ║ ║ MEM: ▓▓▓▓▓▓░░░░ ║ ║ GPU: ▓▓▓▓▓▓▓░░░ ║ ║ ║ ║ NET: [CONNECTED] ║ ║ USR: AUTHORIZED ║ ╚═══════════════════╝