Education

PhD 2014	Remote Sensing	University of Connecticut
MS 2009	GIScience	University of Connecticut
BS 2005	Geology	University of Peradeniya, Sri Lanka

Professional Experience

2023-present	Assistant Professor, Department of Natural Resources and the Environment, University of Connecticut
2020 - 2023	Assistant Professor in Residence, Department of Natural Resources and the Environment, University of Connecticut
2018 - 2020	Assistant Research Professor, Department of Natural Resources and the Environment, University of Connecticut
2016 - 2018	Visiting Assistant Professor, Department of Natural Resources and the Environment, University of Connecticut
2014 - 2016	Postdoctoral Research Fellow, Department of Ecology and Evolution, SUNY Stony Brook
2006 - 2013	Graduate Research and Teaching Assistant, Center for Integrative Geosciences, University of Connecticut
2005 - 2006	GIS Analyst, United Nations (UN) Office for the Coordination of Humanitarian Affairs

Courses Taught

NRE 5560	High Resolution Remote Sensing: Applications of UAS & LiDAR
NRE 5525	Remote Sensing of the Environment
NRE 5215	Introduction to Geospatial Analysis with Remote Sensing
NRE 2000	Introduction to Geomatics

Research Interests

My research efforts broadly capture methodological developments and adaptations to unseal faster, deeper, and more accurate analysis of large volumes of multi-modal remote sensing data for environmental, industrial, and agricultural applications. My research scope is global and has a greater emphasis on Arctic Permafrost remote sensing. I harness sub-meter resolution satellite imagery, AI, and high-performance computing resources to map permafrost landforms, monitor thaw disturbances, and assess risk to human-built infrastructure in the Arctic.  Thinking beyond its research and industrial merits, I always value the strengths of remote sensing to address the requirements of the next generation science standards via the key elements from physics and engineering. To this end, I am actively seeking creative ways, such as imagery-enabled lesson plans to harness remote sensing in K-12 STEM education. 

Teaching Philosophy

I aim to harness cutting-edge geospatial innovations & technologies as transformative learning instruments to nurture students’ learning competencies to holistically understand complex interactions between human and environment and to scientifically query how those interactions simultaneously shape ecological equilibrium, economic prosperity, and social wellbeing. I am a strong advocate of the idea that - most part of knowledge comes through fingers rather through eyes.  It is my goal that students completing my course(s) depart with the knowledge, skills, and especially the motivation to help them to become successful professionals and – equally important – informed citizens. Within my teaching space, I believe that my role is not only one as a teacher, but also one as a facilitator, encouraging students to question, examine, explore, and hopefully develop the level of enthusiasm – and love – that I have for what I teach. 

Current or Pending Recent Research Grants

Awarded (+$14 million)

• • PI: Operationalizing drone imaging technology to detect nutrient deficiencies in fruit orchards. USDA/NIFA Crop Protection and Pest Management. 2023-2026. ($200,000). 
  • PI: Multi-source remote sensing data for modelling tree risk on utility infrastructure and leveraging climate adapted vegetation management. Eversource Energy, 2023-2026. ($275,000). 
  • PI: Predictive Modelling of Domestic Well Water Quality Using Artificial Intelligence. CT Institute of Water Resources/USGS. 2025-2026 ($60,000) 
  • Co-PI (UConn): Collaborative Research: The Permafrost Discovery Gateway: Navigating the New Arctic Tundra through Big Imagery, Artificial Intelligence and Cyberinfrastructure. (U. Connecticut, U. Alaska-Fairbanks, U.  Illinois Urbana-Champaign). NSF Office of Polar Programs – Navigating the New Arctic. 2019-2024. ($3,000,000). 
  • Co-PI(UConn): Collaborative Research: The role of capillaries in the Arctic hydrologic system. NSF Office of Polar Programs – Arctic Natural Sciences. (U. Connecticut, Woodwell Climate Research Center, U. Mass-Amherst, U. Alaska-Fairbanks). 2023-2026. ($2,000,000). 
  • Co-PI: Tracking Arctic permafrost thaw with GeoAI to inform climate action. (U. Connecticut, Woodwell Climate Research Center, UC Santa Barbara, U. Illinois Urbana-Champaign, Alfred Wegner Institute). Google. 2023-2026. ($5,000,000). 
  • Co-PI: Artificial Intelligence Informed High-Resolution Fuel Mapping for Interior Alaska Military Lands to Support Wildfire Forecasting (Univ. of Connecticut, Univ. of Alaska-Fairbanks, Woodwell Climate Research Center). Dept. of Defense. 2025-2027. ($675,000). 
  • PI: Multitemporal Monitoring of Forest Regrowth Using Drone Technology. South Central Connecticut Water Authority. 2024-2025. (~$25,000). 
  • PI: In-state student educational training agreement. Connecticut Office of Policy Management. 2024-2025. ($31,500). 
  • PI: StateView Program Development and Operations for the State of Connecticut. DOI/United States Geological Survey/America View. 2024-2025 (23,500). 
  • PI: StateView Program Development and Operations for the State of Connecticut. DOI/United States Geological Survey/America View. 2023-2025 (23,500). 
  • Co-PI: CAHNR Teaching Enhancement Grant. 2024. ($16,500). 
  • PI: Development of a Continuous Aquatic Plant Tracking And Imaging Network (CAPTAIN) to monitor surface water bodies in Connecticut. Connecticut Institute of Water Research/USGS. 2023-2024. ($48,000). 
  • PI: Multitemporal Monitoring of Forest Regrowth Using Drone Technology. South Central Connecticut Water Authority. 2023-2024. (~$25,000). 
  • Co-PI: Phase I IUCRC University of Connecticut: Center for Hardware and Embedded Systems Security and Trust. NSF IUCRC Indust-Univ Coop Res Ctr. 2019 – 2024 ($749,000). 
  • PI: Mapping Beaver Activity in the Eightmile River Watershed using Multi-source Remote Sensing Data. Eightmile River Watershed. 2023-2024. ($9,600). 
  • PI: Mapping Modelling of Aboveground Forest Carbon Stocks in Connecticut using LiDAR and Forest Inventory Analysis Data. USDA/McIntire-Stennis. 2021-2024. ($60,000). 
  • Co-PI: Climate Change Impact on Beavers and Their Effect on Connecticut Forests. USDA/McIntire-Stennis. 2023-2026. ($60,000). 
  • Co-PI: Green energy Development and carbon mitigation potential of forests and working lands. Eversource Energy. 2021-2023. ($50,000).  
  • PI: Unmanned Aerial System, UConn CAHNR Equipment Grant. 2022 ($61,237).  
  • PI (UConn): Collaborative Research: Patterns, Dynamics, and Vulnerability of Arctic Polygonal Ecosystems: From Ice-Wedge Polygon to Pan-Arctic Landscapes, NSF Office of Polar Programs – Arctic System Science. (Univ. of Connecticut, Univ. of Alaska-Fairbanks, Univ. of Virginia). 2018-2021. ($1,300,000).  
  • PI: Assessing Forest Risk to Infrastructure Using Aerial, UAS, and LiDAR Data, Eversource Energy. 2020 -2023. ($292, 000) 
  • PI: Protecting Critical Infrastructure from UAV Threats (Phase II) – Developing an Integrated Multi-Sensor System for UAV Detection.  Eversource Energy. 2019-2020. ($391,000). 
  • PI: Protecting Critical Infrastructure from UAV Threats (Phase I). Eversource Energy. 2018-2019. ($150,000). 
  • PI: Evaluation of LiDAR and Alternative Technologies for Monitoring Roadside Vegetation and Utility Infrastructure (Phase III). Eversource Energy. 2018-2020. ($245,000). 
  • PI: Detection of potato leafhopper damage using unmanned aerial systems. USDA/Northeastern IPM Center. ($48,000). 2021-2023, 
  • PI: Monitoring Stand- and Tree-Level Forest Change and Plant Invasions using Satellite, Aircraft, UAS Imaging Technologies and Advanced Data Analytics. South Central Connecticut Regional Water Authority. 2020-2021. ($15,000). 
  • PI: Harnessing big satellite imagery, deep learning, and high-performance computing resources to map pan-Arctic permafrost thaw. Leadership Research Allocation Award. Frontera/NSF. 2020-2021. (40,000 GPU hours). 
  • PI: Big imagery, artificial intelligence, and high-performance computing as resources to understand the evolution of pan-Arctic polygonal tundra. Extreme Science and Engineering Discovery Environment (XSEDE)/NSF. [DPP190001], 2020-2021. (40,000 GPU hours). 
  • PI: Big imagery, artificial intelligence, and high-performance computing as resources to understand the evolution of pan-Arctic polygonal tundra. Extreme Science and Engineering Discovery Environment (XSEDE)/NSF. [DPP190001], 2020-2021. (20,000 GPU hours) 
  • PI: State View Program Development and Operations for the State of Connecticut. DOI/United States Geological Survey/America View. 2019-2020. ($23, 500). 
  • PI: State View Program Development and Operations for the State of Connecticut. DOI/United States Geological Survey/America View. 2018-2019. ($23, 500). 
  • PI: High performance image analysis for fine-scale characterization of Pan-Arctic ice-wedge polygons from sub-meter commercial satellite imagery, Extreme Science and Engineering Discovery Environment (XSEDE)/NSF. [TG-DPP180003]. 2018-2019. (5,000 GPU Hours) 
  • PI: Exploring opportunities to develop a web-based adaptive learning environment to harness remote sensing in Connecticut’s K-12 education. DOI/United States Geological Survey/America View. 2017-2018. ($7,500).  
  • PI: Integrated Systems Research and Development in Automation and Sensors for Sustainability of Specialty Crops. USDA/NIFA - Hatch Multistate. 2018-2021 ($90,000). 
  • Co-PI: Assessing Temporal Dynamics of Disturbance Interactions as a Driver of a Novel Forest Mortality Event. NSF Division of Environmental Biology. 2019-2020. ($58,000) 
  • Co-PI: Promoting Water Conservation with Remote Sensing and Hydrological Modeling. Eversource Energy. 2019-2020. ($150,000) 
  • Co-PI: Evaluation of Airborne and Mobile LiDAR Technologies for Monitoring Roadside Vegetation and Utility Infrastructure (Phase II). Eversource Energy. 2018-2019. ($245,000). 
  • Co-PI: Creating Catalog of Point Clouds for Public Buildings in Enfield, Connecticut. National Institute of Standards and Technology. 2018-2020. ($130,000). 
  • Co-PI: Evaluation of Airborne and Mobile LiDAR Technologies for Monitoring Roadside Vegetation and Utility Infrastructure (Phase I). Eversource Energy. 2016-2018. ($338,000). 
  • Co-PI: Development of a model system for scouting potato leafhopper using unmanned aerial system technology. UConn Office of the Vice President for Research. 2018-2019. ($50,000). 

Submitted 

• Co-PI: AI & Earth Observation for Equitable Youth Civic Engagement: A Transdisciplinary Approach to Systems Transformation. Spencer Foundation. ($75,000). 2026-2027. 

• Co-PI: Collaborative Research: PACSP TOOLS: Fine scale mapping of Fagus grandifolia (American beech) distribution for targeted restoration of areas impacted by beech leaf disease. NSF Partnership to Advance Conservation Science and Practice & Paul G. Allen Family Foundation. ($2,000,000). 2025-2029. 

• Co-PI: Raices Verdes: Empowering Puerto Rican and Latino Youth through Nature and Innovation. NSF Division of Research on Learning in Formal and Informal Settings. ($1,800,000). 2025-2028. 

• Co-PI: Collaborative Research: A new dynamic equilibrium for river networks: how beaver created ecosystems change carbon storage and methane emissions. NSF Macrosystems. ($2,000,000). 2025-2028. 

• PI: Development of a Grapevine Early Disease Identification (GEDI) System. USDA/NIFA Hatch. 2025 – 2028. ($60,000).  

• Co-PI: Harnessing AI & multi-modal remote sensing data to improve aboveground forest biomass estimates in New England’s mixed temperate forest.  USDA/NIFA McIntire-Stennis. 2025-2028. ($60,000). 

• Co-PI: Forest Clearings: The Spatial Logics of Management. Scholarship and Collaboration in Humanities and Arts Research, University of Connecticut Office of the Vice President Research. 2025-2026. ($50,000) 

Publications

Google Scholar Profile 

• Manos, E., Witharana, C. & Liljedahl, A. K. (2025) Permafrost thaw-related infrastructure damage costs in Alaska are projected to double under medium and high emission scenarios. Communications Earth & Environment, 6, 221. 

• Joshi, D. & Witharana, C. (2025) Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty. Remote Sensing, 17, 1066. 

• Tanzer, D. N., Witharana, C. & Fahey, R. T. (2025) Classification of tree mortality following drought-defoliation interaction using single date Landsat imagery and comparison to aerial detection surveys. International Journal of Applied Earth Observation and Geoinformation, 139, 104488. 

• Yang, Y., Rodenhizer, H., Rogers, B. M., Dean, J., Singh, R., Windholz, T., Poston, A., Potter, S., Zolkos, S., Fiske, G., Watts, J., Huang, L., Witharana, C., Nitze, I., Nesterova, N., Sophia Barth, G. G., Lantz, T., Runge, A., Lombardo, L., Nicu, I. C., Rubensdotter, L., Makopoulou, E. & Natali, S. (2025) A Collaborative and Scalable Geospatial Data Set for Arctic Retrogressive Thaw Slumps with Data Standards. Nature Scientific Data, 12, 18. 

• Abolt, C., Atchley, A., Harp, D., Liljedhal, A.K., Jorgenson, T., Witharana, C., Bolton, W., Jon, S., Rettlebach, T., Gross, G., Boike, J., Nitze, Rumpca, C., Wilson, C., Benettt, C. (2024).Topography controls variability in circumpolar permafrost thaw pond expansion Journal of Geophysical Research - Earth Surface 

• Nitz, I., Nitze, I., van der Sluijs, J., Barth, S., Bernhard, P., Huang, L., Lara, M., Witharana, C. (2024). A labeling intercomparison of retrogressive thaw slumps by a diverse group of domain experts. Permafrost and Periglacial Processes.  

• Cranmer, N., Fahey, R.T, Worthley, T., Witharana, C., Bunce, A., (2024). Tree Trimming Effects on 3-Dimensional Crown Structure and Tree Biomechanics: A Pilot Project. Arboriculture & Urban Forestry. 

• Liljedahl, A. K., Witharana, C., & Manos, E. (2024). The capillaries of the Arctic tundra. Nature Water, 1-4. https://doi.org/10.1038/s44221-024-00276-9. 

• Wedagedara, H., Witharana, C., Fahey, R., Cerrai, D., Parent, J., & Perera, A. S. (2024). Non-Parametric Machine Learning Modeling of Tree-Caused Power Outage Risk to Overhead Distribution Powerlines. Applied Sciences, 14(12), 4991.  

• Perera, A. S., Witharana, C., Manos, E., Liljedahl, A.K., (2024). “Hyperparameter Optimization for Large-Scale Remote Sensing Image Analysis Tasks: A Case Study Based on Permafrost Landform Detection Using Deep Learning.” IEEE Access 12:43062–77.  

• Worthley, T., Bunce, A., Morzillo, A. T., Witharana, C., Zhu, Z., Cabral, J., Wedagedara, H. M. & Fahey, R. T. (2024). Stormwise: Innovative Forest Management to Promote Storm Resistance in Roadside Forests. Journal of Forestry. 122(4). https://doi.org/10.1093/jofore/fvae011 

• Nitze, I., van der Sluijs, J., Barth, S., Bernhard, P., Huang, L., Lara, M., Witharana, C, et al. (2024). A labeling intercomparison of retrogressive thaw slumps by a diverse group of domain experts. EarthArXiv. doi.org/10.31223/X55M4P. 

• Li, W., Hsu, C.-Y., Wang, S., Yang, Y., Lee, H., Liljedahl, A., (2024). Segment anything model cannot segment anything: Assessing ai foundation model generalizability in permafrost mapping. Remote Sensing, 16(5), 797. 

• Manos, E., Witharana, C., Perera, A. S., & Liljedahl, A. K. (2023). A multi-objective comparison of CNN architectures in Arctic human-built infrastructure mapping from sub-meter resolution satellite imagery. International Journal of Remote Sensing, 44(24), 7670-7705.  

• Webb, E. E., Liljedahl, A. K., Loranty, M. M., Witharana, C., & Lichstein, J. W. (2023). Reply to: Detecting long-term Arctic surface water changes. Nature Climate Change, 13(11), 1194-1196. 

• Wedagedara, H., Witharana, C., Fahey, R., Cerrai, D., Joshi, D., & Parent, J. (2023). Modeling the impact of local environmental variables on tree-related power outages along distribution powerlines. Electric Power Systems Research, 221, 109486.  

• Li, W., Hsu, C.-Y., Wang, S., Witharana, C., & Liljedahl, A. (2023). Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features. arXiv preprint arXiv:2306.05341. https://doi.org/10.48550/arXiv.2306.05341  

• Sellers, H. L., Vargas Zesati, S. A., Elmendorf, S. C., Locher, A., Oberbauer, S. F., Tweedie, C. E., Witharana, C., Hollister, R. (2023). Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska? Remote Sensing, 15(8), 1972.  

• Webb, E.E., Liljedhal, A.K., Cortdiro, J.A., Loranty, M.M., Witharana, C. Lichstein, J.W. (2022) Permafrost Thaw Drives Surface Water Drainage Across the Pan-Arctic. Nature Climate Change. https://doi.org/10.1038/s41558-022-01455-w  

• Jorgenson, M. T., Kanevskiy, M. Z., Jorgenson, J. C., Liljedahl, A., Shur, Y., Epstein, H., Kent, K., Griffin, C.G., Daanen, R., Boldenow, M., Orndahl, M., Witharana C., Jones, B.M. (2022). Rapid transformation of tundra ecosystems from ice-wedge degradation. Global and Planetary Change, 216, 103921.  

• Hasan, A., Udawalpola, M., Liljedahl, A., & Witharana, C. (2022). Use of Commercial Satellite Imagery to Monitor Changing Arctic Polygonal Tundra. Photogrammetric Engineering & Remote Sensing, 88(4), 255-262.  

• Witharana C, Udawalpola MR, Liljedahl AK, Jones MKW, Jones BM, Hasan A, Joshi D, Manos E. (2022) Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery. Remote Sensing. 2022; 14(17):4132. https://doi.org/10.3390/rs14174132. 

• Manos, E., Witharana, C., Udawalpola, M. R., Hasan, A., & Liljedahl, A. K. (2022). Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery. Remote Sensing, 14(11), 2719. https://doi.org/10.3390/rs14112719. 

• Udawalpola, M. R., Hasan, A., Liljedahl, A., Soliman, A., Terstriep, J., & Witharana, C. (2022). An Optimal GeoAI Workflow for Pan-Arctic Permafrost Feature Detection from High-Resolution Satellite Imagery. Photogrammetric Engineering & Remote Sensing, 88(3), 181-188. https://doi.org/10.14358/PERS.21-00059R2. 

• Hasan, A., Udawalpola, M., Liljedahl, A., & Witharana, C. (2022). Use of Commercial Satellite Imagery to Monitor Changing Arctic Polygonal Tundra. Photogrammetric Engineering & Remote Sensing, 88(4), 255-262. https://doi.org/10.14358/PERS.21-00061R2. 

• Parent, J. R., Witharana, C., & Bradley, M. (2022). Classifying and Georeferencing Indoor Point Clouds With ArcGIS. Photogrammetric Engineering & Remote Sensing, 88(6), 383-390. 

• Jones, B. M., Tape, K. D., Clark, J. A., Bondurant, A. C., Ward Jones, M. K., Gaglioti, B. V., Witharana. C. (2021). Multi-dimensional remote sensing analysis documents beaver-induced permafrost degradation, Seward Peninsula, Alaska. Remote Sensing, 13(23), 4863. 

• Suh, J. W., Anderson, E., Ouimet, W., Johnson, K. M., & Witharana, C. (2021). Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data. Remote Sensing, 13(22), 4630. 

• Zhang, J., Shang, R., Rittenhouse, C., Witharana C., Zhu, Z. (2021). Evaluating the impacts of models, data density and irregularity on reconstructing and forecasting dense Landsat time series. Science of Remote Sensing 4(2021)100023. https://doi.org/10.1016/j.srs.2021.100023. 

• Witharana C., M. A. E. Bhuyian, A.K. Liljedahl, M. Kanevsky, T, Jorgenson, B. M. Jones, R. Daanen, H.E. Epstein, C. G. Griffin, K. Kent, M. K. Ward Jones. (2021). An object-based approach for mapping tundra ice-wedge polygon troughs from very high spatial resolution optical satellite imagery. Remote Sensing 13(4). DOI: https://doi.org/10.3390/rs13040558. 

• Stryker, N., M. Wethington, A. Borowicz, S. Frrest, C. Witharana, T. Hart, H.J. Lynch. (2020). A global population assessment of the Chinstrap penguin (Pygoscelis antarctica). Nature Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-76479-3. 

• Bhuiyan, M. A. E., C. Witharana, A.K. Liljedahl. (2020). Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. Journal of Imaging 6(12). https://doi.org/10.3390/jimaging6120137. 

• Witharanaa, C. M. A. E. Bhuyian, A.K. Liljedahl, M. Kanevsky, T, Jorgenson, B. M. Jones, R. Daanen, H.E. Epstein, C. G. Griffin, K. Kent, M. K. Ward Jones. (2020). Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection. ISPRS Journal of Photogrammetry and Remote Sensing 170(174-191). https://doi.org/10.1016/j.isprsjprs.2020.10.010. 

• Bhuiyan, M.A.E., C. Witharana, A.K. Liljedahl, B. M. Jones, R. Daanen, H.E. Epstein, C. G. Griffin, K. Kent, M. K. Ward Jones. A. Agnew. (2020). Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery. Journal of Imaging 6(9). https://doi.org/10.3390/jimaging6090097. 

• Yang, Z., C. Witharana, J. Hurd, R. Hao, S. Tong. (2020). Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA. Journal of Environmental Earth Sciences 79(18). https://doi.org/10.1007/s12665-020-09159- 0. 

• Parent J., T. Meyer. J. Volin, R. Fahey, C. Witharana. (2019). An Analysis of Enhanced Tree Trimming Effectiveness using a Geospatial Approach. Journal of Environmental Management. doi.org/10.1016/j.jenvman.2019.04.027. 

• Zhang, W., C. Witharana, A.K. Liljedahl, M. Kanevskiy. (2018). Deep convolutional neural networks for automated characterization of Arctic ice-wedge polygons in very high spatial resolution aerial imagery, Remote Sensing. 

• Zhang, W., C. Witharana, W. Li, C. Zhang , X. Li, J. Parent. (2018). Using deep learning to identify geographic objects and estimate their locations from Google Street View images: A case study of utility poles with crossarms. Sensors. 

• Witharana, C., W. B. Ouimet, K. M. Johnson, K.M. (2018). Using LiDAR and GEOBIA for automated extraction of 18th-late 19th century relict charcoal hearths in southern New England. GIScience & Remote Sensing: doi.org/10.1080/15481603.2018.1431356. 

• Witharana, C., Ma. A LaRue, H.J. Lynch, (2016). Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring. ISPRS Journal of Photogrammetry and Remote Sensing, 113: 124-143. 

• Witharana, C., H.J. Lynch. (2016). An object-based image analysis approach for detecting penguin guano from very high spatial resolution satellite images. Remote Sensing 8(5): 375. 

• Witharana, C., D. L. Civco., T. Meyer. (2014). Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows, ISPRS Journal of Photogrammetry and Remote Sensing, 87(2014):1-18. 

• Witharana, C. and D. L. Civco. (2014). Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of a supervised discrepancy measure, ISPRS Journal of Photogrammetry and Remote Sensing, 87(2014):108-121. 

• Witharana, C., D. L. Civco, T. Meyer. (2013). Evaluation of Pan sharpening Algorithms in Support of earth observation based rapid mapping workflows, Applied Geography, 37(2013):63-87. 

• Witharana, C., (2012). Who does what where? Advanced earth observation for humanitarian crisis management. IEEE 6th International Conference on Information and Automation for Sustainability. Beijing, China. https://doi.org/10.1109/ICIAFS.2012.6420035. 

Conferences

• • Witharana, C., Manos, E., Perera, A., Pimenta, M., Liljedahl, A.K., Satellite Imagery and GeoAI in Action at Pan-Arctic Scale, AGU Annual Meeting 2024, Washington DC. 
  • Manos, E., Witharana, C., and Liljedahl, A.K., Deep learning-based building and road detection reveals higher permafrost thaw-related damage costs than previously estimated for Alaska, Fall Meeting, AGU, 2024, Washington, DC.  
  • Perera A.S., Fernandez D., Witharana C., Manos E., Pimenta M., Nicholson T., Liljedhal A., Pan-Arctic Permafrost Landform Feature Detection with Vision Transformers, Advancing Machine Learning for Remote Sensing: Overcome Data Scarcity and Domain Shift Challenges, AGU Annual Meeting, 2024, Washington DC.  
  • Himandi, S. and Witharana, C., 2024, September. Converging LiDAR, Sentinel-2, and NAIP Data with Machine Learning for Forest Aboveground Biomass Modeling. Annual SAF Convention 2024, Loveland, Colorado.  
  • Pimenta, M., and Witharana, C., 2024, Hydro-geomorphological dynamics in Alaskan ice wedge polygons: A deep learning approach: Geological Society of America Abstracts  
  • Pimenta, M., Witharana, C., Manos, E., and Perera, A., 2024, Hydro-geomorphological dynamics in Alaskan ice wedge polygons: A deep learning approach: 52nd International Arctic Workshop, Program and Abstracts, Dept of Earth, Geographic, and Climate Sciences, University of Massachusetts-Amherst, 210 p.  
  • Zocco, E., Witharana, C., Himandi, S., 2023, Mapping of Invasive Understory Plants in Connecticut Forests using Multitemporal Satellite Imagery. Annual CLCC Workshops, Middletown, CT. [Mar 25, 2023]. 
  • Zocco, E., Witharana, C., Ortega, I., Beaver Disturbance Assessment and Mapping [B-DAM]. NorthEast Arc Users Group, West Kingston, RI [May 15-16, 2023] 
  • occo, E., Witharana, C., Ortega, I., Beaver Habitats Decoded: Deploying Deep Learning for Beaver Habitat Detection in Connecticut. ASM Annual Meeting, Boulder, CO [June 7-11, 2024] 
  • Himandi, S. and Witharana, C., 2024, November. Optimizing Parametric and Non-Parametric Algorithms to Estimate Forest Aboveground Biomass from Forested Inventory Analysis and remote sensing observations. (Will be presenting in November 2024) Forest Inventory and Analysis Science Symposium.  
  • Lamichhane, S., Witharana, C., Lentz, E., and Tao, H., Early Assessment of Nutrient Deficiency in Fruit Crops using Unpiloted Aerial System (UAS) Imagery, (will be presenting in October) Fall NEARC 2024 Annual Conference Program, Burlington, Vermont. 
  • Joshi D., Witharana C., Automated detection and geolocation of hazard trees along the electric powerlines. ASPRS Geo Week 2024, Denver, Colorado. [Feb 11-13, 2024].  
  • Wedagedara H., Witharana C., Fahey R., Cerrai D., Joshi D., Parent J., Modeling the impact of different variables on tree-related power outages along distribution powerlines using machine learning techniques. Eversource Energy Center Annual Workshop 2023, University of Connecticut. [Apr 26, 2023]. 
  • Himandi, S. and Witharana, C., 2023, November. Seeing the Unseen: Mapping Invasive Understory Plants in Connecticut Forests Using Multitemporal Satellite Imagery. GIS Day 2023 University of Connecticut, Waterbury. [Nov 15, 2023] 
  • Himandi, S. and Witharana, C., 2023, Mapping of invasive understory plants in Connecticut forests using multitemporal satellite imagery. Annual CLCC Workshops, Middletown, CT. [March, 2023] (Workshop Presentation) 
  • Wedagedara H., Witharana C., Modeling the tree-related outage risk along distribution powerlines using non-parametric machine learning algorithms. GIS Day 2023 University of Connecticut, Waterbury. [Nov 15, 2023]. 
  • Abolt, C., Atchley, A. L., Harp, D. R., Liljedahl, A. K., Jorgenson, T., Witharana, C., (2023). Topography is a more important control than air temperature on circumpolar variability in thermokarst pool expansion. AGU Annual Meeting, San Francisco, CA [Dec 11-15, 2023].  
  • Witharana, C., Mapping retrogressive thaw slumps in High Arctic Canada using high-spatial resolution satellite imagery and deep learning. 16th International Circumpolar Remote Sensing Symposium. Fairbanks, AK. May. 19. 2022. 
  • Wedagedara H., Joshi D., C. Witharana, D., Cerrai, D., Fahey, R., D., Cerrai, D., Fahey, R., Machine Learning Modelling for Predicting Roadside Forest Risk on Distribution Powerlines, AGU 2022 Fall meeting, Chicago, IL. [Dec 12-16, 2022]. 
  • Hasan, A., Witharana, C. A U-Net based algorithm for automated detection of clouds from medium-resolution satellite imagery. 16th International Circumpolar Remote Sensing Symposium. May. 17. 2022. Fairbanks, AK. 
  • Agnew, A, Witharana, C., Legrand, A., Early detection of potato leafhopper damage using unmanned aerial systems. Entomological Society of America. Apr. 14. 2022. Philadelphia, PA. 
  • Udawalpola, M.R., Witharana, C., Automated Recognition of Permafrost Disturbances using High-spatial Resolution Satellite Imagery and Deep Learning Models. ASPRS Annual Conference. Mar. 23. 2022. (virtual) 
  • Joshi, D.P., Witharana, C., Roadside Forest Modelling using Dashcam Videos and Convolutional Neural Nets. ASPRS Annual Conference. Mar. 23. 2022. (virtual) 
  • Wedegedara, H., Witharana, C., Geospatial Modeling of Roadside Vegetation Risk on Distribution Power Lines in Connecticut. ASPRS Annual Conference. Mar. 23. 2022. (virtual) 
  • Himandi, S. and Witharana, C., 2022, December. Combining Air Borne LiDAR and Forest Inventory Analysis Data (FIA) to Develop a Forest Carbon Model Using Machine Learning Techniques. In AGU Fall Meeting Abstracts (Vol. 2022, pp. INV25C-0535).  
  • Himandi, S. and Witharana, C., 2022, November. Machine learning based modelling of forest above ground biomass in Connecticut using airborne LiDAR and forest inventory analysis (FIA) data. Fall NEARC 2022 Annual Conference Program, New Hampshire. [Nov 6-9, 2022] (Poster Presentation). 
  • Udawalpola, M.R., A. Hasan, A.K. Liljedahl, C. Witharana. High performance image analysis workflow designs for automated mapping of ice-wedge polygons from high-resolution satellite imagery. Regional Conference on Permafrost and 19th International Conference on Cold Regions Engineering, co-organized by US Permafrost Association and American Society of Civil Engineers (Virtual). Oct 24 – 29, 2021. 
  • Hasan, A., Udawalpola, M.R., A.K. Liljedahl, C. Witharana. Understanding the effect of image augmentation on deep learning convolutional neural net algorithms. Regional Conference on Permafrost and 19th International Conference on Cold Regions Engineering, co-organized by US Permafrost 
  • Manos, E., A. Hasan, M.R. Udawalpola, A.K. Liljedhal, C. Witharana. Automated recognition of ice-wedge polygon troughs and human-built infrastructure in the Arctic permafrost landscapes using commercial satellite imagery. Regional Conference on Permafrost and 19th International Conference on Cold Regions Engineering, co-organized by US Permafrost Association and American Society of Civil Engineers (Virtual). Oct 24 – 29, 2021. 
  • Udawalpola, M.R., A. Hasan, A.K. Liljedahl, C. Witharana. High performance image analysis workflow designs for operational-scale Arctic permafrost mapping applications. AGU Fall Meeting, New Orleans & Online Everywhere. Dec 13 - 17, 2021. 
  • Hasan, A., Udawalpola, M.R., A.K. Liljedahl, C. Witharana.. Understanding the effect of image augmentation methods on automated recognition of permafrost features from high-resolution satellite imagery. AGU Fall Meeting, New Orleans & Online Everywhere. Dec 13-17, 2021. 
  • Joshi, D., H. Wedegedara, J. Parent, C. Witharana.. Geospatial Modelling of Vegetation Risk on Electric Utility Infrastructure in Connecticut. AGU Fall Meeting, New Orleans & Online Everywhere. Dec 13 -17, 2021. 
  • Manos, E., A. Hasan, M.R. Udawalpola, A.K. Liljedahl, C. Witharana. Automated Recognition of Human- Built Infrastructure in the Arctic Permafrost Landscapes using Commercial Satellite Imagery. AGU Fall Meeting, New Orleans & Online Everywhere. Dec 13 -17, 2021. 
  • Liljedahl, A.K., M. B. Jones, A. E. Budden, C. Witharana, J. M. Cervenec, .M. Jones, C.S. Jones, L. Marinin, L. Walker, A. Hasan, I. Nitze, G. Wind, M. Brubaker, R. Thiessen-Bock, K. McHenry. The Permafrost Discovery Gateway. AGU Fall Meeting, New Orleans & Online Everywhere. Dec 13 -17, 2021. 
  • Witharana, C., M. A. E. Bhuiyan, A.K. Liljedahl. M. Kanesvskiy, T. Jorgenson, B. M. Jones, R. Daanen, W. E. Epstien, C. G. Griffin, K. Kent, M. K. Ward Jones. (2020). Automated Mapping of Ice-wedge Polygon Troughs in the Continuous Permafrost Zone using Commercial Satellite Imagery. AGU Fall Meeting (virtual). Pages C013-0002. 
  • Bhuiyan, M. A. E., C. Witharana, A. K. Liljedahl. (2020). Harnessing Commercial Satellite Imagery, Artificial Intelligence, and High Performance Computing to Characterize Ice-wedge Polygonal Tundra. AGU Fall Meeting (virtual). 
  • Liljedhal, A.K., B. M. Jones, M. Brubaker, A. E. Budden, J. M. Cervenec, G. Grosse, M. B. Jones, L. Marrinii, K. MHnery, J. Moss, P. J. Morin, I. Nitze, A. Soliman, G. Wind, C. Witharana. (2020). Permafrost Discovery Gateway: A web platform to enable discovery and knowledge-generation of permafrost Big Imagery products. AGU Fall Meeting (Virtual). Pages C13E-1373. 
  • Giulia, S., X. Shen, C. Witharana, E. Anagnostou. (2020). A framework for integrating Lidar, satellite and weather observations to support improvements in residential irrigation. AGU Fall Meeting (virtual). pages C004-0008. 
  • Bhuiyan, M. A. E., C. Witharana, A. K. Liljedahl. (2020). Big Imagery as a Resource to Understand Patterns, Dynamics, and Vulnerability of Arctic Polygonal Tundra. AGU Fall Meeting (virtual). Pages C13E-1374. 
  • Witharana, C. (INVITED Talk). (2019). Big imagery, AI, and HPC as resources to understand patterns, dynamic, and vulnerability of Arctic polygonal tundra. Dept. of Biological Sciences,, University of Texas – EL Paso, May 2019. 
  • Witharana, C. (INVITED Talk). (2019). Big imagery as a resource to understand patterns, dynamic, and vulnerability of Arctic polygonal tundra, Artificial Intelligence working group, Institute for Advanced Computational Science, Stony Brook University, NY, April 2019. 
  • Witharana, C, W., Bhuiyan, M.A.E., Liljedahl, 2018, Towards first pan-Arctic ice-wedge polygon map: Understanding the synergies of data fusion and deep learning in automated ice-wedge polygon detection from high resolution commercial satellite imagery, AGU Fall Meeting, San Francisco, CA. 
  • Witharana C., W. Zhang. 2019. Automated Characterization of Ice-Wedge Polygons from Very-High-Resolution Imagery, ASPRS Annual Conference (IGTF2019), Denver, CO. 
  • Witharana C., W. Ouimet, 2019, Object-Based Detection of Southern New England 18th to late 19th-Century  Charcoal Hearths from Lidar Data, ASPRS Annual Conference (IGTF2019), Denver, CO. 
  •  Witharana C., J. Parent, 2019, Automated Mapping of Electric Utility Infrastructure from Multi-Source Remote Sensing Data. ASPRS Annual Conference (IGTF2019), Denver, CO. 
  • Witharana, C, W. Zhang, A.K. Liljedahl, 2018, From Ice-Wedge Polygons to Pan-Arctic Landscapes: Intensive Mapping of Ice-Wedge Polygons at Extensive Spatial Scales Using Very High-Resolution Remote Sensing Imagery, AGU Fall Meeting, Washington DC. 
  • Witharana, C,. November 2017, Remote Sensing meets K-12 STEM curricula, Pecora20, Sioux Falls, SD. 
  • Witharana, C, November 2017, All quiet on the Northern Front: Remote sensing based retrospection of human wellbeing in the armed-conflicted areas of Sri Lanka, Pecora20, Sioux Falls, SD. 
  • Witharana, C, March 2017, LiDAR, GEOBIA, and Archaeology: Remote sensing of southern New England’s lost archaeological landscape, ASPRS Annual Conference (IGTF2017), Baltimore, MD. 
  • Witharana, C, March 2017, Transferability of GEOBIA rulesets for detecting penguin guano in very high spatial resolution satellite images, ASPRS Annual Conference (IGTF2017), Baltimore, MD. 
  • Witharana, C, April 2016, Real-time streaming data on penguin abundance and distribution using very high spatial resolution satellite imagery, ASPRS Annual Conference, Fort worth, TX.  
  • Witharana, C, April 2016, Geospatial technologies meet K-12 STEM curricula: Use of Remote Sensing as a pedagogical tool in earth and environmental science education, ASPRS Annual Conference, Fort Worth, TX.  
  • Witharana, C, October 2016, Real-time streaming of penguin abundance and distribution: Automated workflows for satellite imagery interpretation, analysis, and visualization, Scientific Committee on Antarctic Research (SCAR)  2016, Kuala Lumpur, Malaysia. 
  • Witharana, C., D. Tiede, D.L. Civco.(2013). Optimizing multi-resolution segmentation algorithm using empirical methods: Exploring the sensitivity of supervised discrepancy measures. SPIE Remote Sensing Europe, Dresden, Germany. 
  • Witharana, C. (2013) High resolution remote sensing’s Achilles heel: Linking image objects (this is a 'thing') and high-level semantics (what is this 'thing'?). Image Processing and Computer Vision for Ecology, Annual Conference, Ecological Society of America, Baltimore, MD. 
  • Witharana, C., M. Neubert, D.L. Civco (2013). Value-added humanitarian information delivery from earth observation data: Investigating synergies of data fusion and image segmentation in rapid mapping workflows. SPIE Remote Sensing Europe, Dresden, Germany. 
  • Witharana, C., D.L. Civco. (2012). Evaluating remote sensing image fusion algorithms for use in humanitarian crisis management, SPIE Remote Sensing Europe, Edinburgh, United Kingdom. 
  • Witharana, C., D.L. Civco, T.H. Meyer. (2012). Who Does What Where? Advanced Earth Observation for Humanitarian Relief Coordination. ASPRS Annual Conference, Sacramento, CA. 
  • Witharana, C., T. Meyer. (2010) Developing customized ArcGIS tools for disaster management. IEEE International Conference Information and Automation for Sustainability, Colombo, Sri Lanka. 
  • Witharana, C., T. Meyer, D.L. Civco, J. Osleeb, 2010. Developing a new ArcGIS Tool to Quantify Building-Content Vulnerability from Storm-Surge Inundation. ASPRS Annual Conference, San Diego, California, USA. 
  • Witharana, C., T. Meyer, D.L. Civco J. Osleeb, 2010. Developing customized ArcGIS tools for disaster management. ESRI International User Conference, San Diego, California, USA. 

Professional Service

• • Director, Remote Sensing & Geospatial Data analytics Grad Program, 2021 -present 
  • Steering Committee Member, UConn Data Science Masters Program, 2021- present 
  • Editorial Advisory Board Member: ISPRS Journal of Photogrammetry and Remote Sensing                                                                 May 2016 – present. 
  • Proposal Review Panelist: NSF Arctic & Antarctic Terrestrial Panel, Fall 2023.  
  • Proposal Review Panelist:  Environmental Data Science, American Association for the Advancement of Science (AAAS), April 2018. 
  • Technical review committee member: 
  • 2020 SIGMAP International Conference on Signal Processing and Multimedia Applications  
  • 2014   IEEE International Conference on Information and Automation for Sustainability  
  • 2012   IEEE International Conference on Information and Automation for Sustainability 
  • 2010   IEEE International Conference on Information and Automation for Sustainability 
  • Reviewer for Peer-reviewed Journals: 

  Nature, Pattern recognition, ISPRS Journal of Photogrammetry and Remote Sensing/  Journal of Applied Geography/ International Journal of Applied Earth Observation and Geoinformation.