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Dr Milto Miltiadou

Research Associate, working with Dr Emily Lines.



  • 2017-2022: Researcher at Civil Engineering and Geomatics, Cyprus University of Technolgy, Cyprus
    • July 2018 seconded at Carbomap, UK
    • April-Jun 2017 seconded at Planetek Italia, IT
    • July-May 2018 seconded at Planetek Hellas, GR
  • 2013-2016: Research Engineer (EngD student) at the Remote Sensing Group of Plymouth Marine Laboratory, UK
    • Jan-Mar 2016 placement at Interpine Innovations Ltd, NZ
  • 2012-2013: Casual Admin Assistant at University of Bath, UK


  • 2013-2017: EngD in Efficient accumulation, analysis and visualisation of Full-waveform LiDAR with applications to forestry at the Department of Computer Science, University of Bath in collaboration with the Remote Sensing Group, Plymouth Marine Laboratory
  • 2012-2013: MSc in Computer Animation and Visual Effects, Bournemouth University
  • 2008-2011: BSc in Computer Science, University of Bristol


  • 2021: “Most Notable Article” in the category “Engineering Remote Sensing” by MDPI Remote Sensing
  • 2021: Distinguished manuscript contribution by Ladies of Landsat
  • 2020: Arctic code Vault Contributor of 2020 GitHub Archive Program
  • 2020: “Audience Favourite Booth Award” at Researcher’s Night in Cyprus
  • 2016: Ede and Ravenscroft Academic Prize of Excellence – Finalist
  • 2015: Student poster Competition at Silvilaser Conference
  • 2012: “You are Brilliant” award – student reps with high involvement


  • 2019-2022: ASTARTE (EXCELLENCE/0918/0341) “Analysis of SAR and thermal satellite data time-series for understanding the long-term impact of land surface temperature changes on forests”- 250,000EUR funded through Cyprus Research Foundation
  • 2018-2022: FOREST (OPPORTUNITY/0916/MSCA/0005) “Advancement of tree structure observation algorithms for forest monitoring” – 150,000EUR funded through Cyprus Research Foundation
  • 2013-2016: EngD studentship “Efficient Accumulation, Analysis and Visualisation of Full-Waveform LiDAR in a Volumetric Representation with Applications to Forestry”- ~100,000GBP funded through the Centre for Digital Entertainment


My research is very multidisciplinary. I have a computer science background, experience in remote sensing and a strong interest for research applications related to forest ecology.

My current project aims to increase scalability of the ecological information we can derived about forest by interpreting and fusing huge amounts of satellite data.

In the past, I worked on detection of dead standing Eucalypt trees for managing biodiversity, efficient data structures for managing LiDAR data during 3D polygonal model creation and time-series SAR data analysis for understanding phenological changes of Paphos forest, Cyprus. I also have a particular interest in the full-waveform LiDAR data, for which I implemented the open-source software DASOS.


Journal articles

  • Miltiadou, M., Karathanassi, V., Agapiou, A., Theocharidis, C., Kolokousis, P., & Danezis, C. (2022). A Selection of Experiments for Understanding the Strengths of Time Series SAR Data Analysis for Finding the Drivers Causing Phenological Changes in Paphos Forest, Cyprus. Remote Sensing, 14(15), 3581. (Available at:
  • Andronis, V., Karathanassi, V., Tsalapati, V., Kolokoussis, P., Miltiadou, M., & Danezis, C. (2022). Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus. Remote Sensing, 14(4), 1010. (Available at:
  • Miltiadou, M., Antoniou, E., Theocharidis, C., & Danezis, C. (2021). Do people understand and observe the effects of climate crisis on forests? The case study of Cyprus. Forests, 12(9), 1152. (Available at:
  • Miltiadou, M., Campbell, N. D., Cosker, D., & Grant, M. G. (2021). A comparative study about data structures used for efficient management of voxelised full-waveform airborne lidar data during 3d polygonal model creation. Remote Sensing, 13(4), 559. (Available at:
  • Martins-Neto, R. P., Tommaselli, A. M. G., Imai, N. N., David, H. C., Miltiadou, M., & Honkavaara, E. (2021). Identification of significative lidar metrics and comparison of machine learning approaches for estimating stand and diversity variables in heterogeneous brazilian atlantic forest. Remote Sensing, 13(13), 2444. (Available at:
  • Miltiadou, M., Agapiou, A., Gonzalez Aracil, S., & Hadjimitsis, D. G. (2020). Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations. Forests, 11(2), 161. (Available at:
  • Miltiadou, M., Campbell, N. D., Aracil, S. G., Brown, T., & Grant, M. G. (2018). Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest. International journal of applied earth observation and geoinformation, 67, 135-147. (Available at:

Conference papers


  • Martins Neto, R. P. (2021). Extraction of structural variables using LiDAR data combined with hyperspectral images for classification of upper canopy tree species in Brazilian Atlantic Forest.
  • Miltiadou, M. (2017). Efficient accumulation, analysis and visualisation of full-waveform LiDAR in a volumetric representation with applications to forestry (Doctoral dissertation, University of Bath).