Dr James G. C. Ball research
Conservation Research Institute and Department of Plant Sciences, University of Cambridge.
Linking cutting edge AI computer vision techniques to remote sensing for tropical forest observation and monitoring.
Python packages
detectree2 - Python package for automatic tree crown delineation based on Mask R-CNN
Selected publications
Ball, J. G. C., Hickman, S. H., Jackson, T. D., Koay, X. J., Hirst, J., Jay, W., … & Coomes, D. A. (2023). Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN. Remote Sensing in Ecology and Conservation, 9(5), 641-655.
Brodie, J.F., Mohd-Azlan, J., Chen, C., Wearn, O., Deith, M., Ball, J. G. C., … & M. Luskin. (2023). Landscape-scale benefits of protected areas for tropical biodiversity. Nature, 620, 807–812.
Cao, Y., Ball, J. G. C., Coomes, D. A., Steinmeier, L., Knapp, N., Wilkes, P., … & Jackson, T. D. (2023). Benchmarking airborne laser scanning tree segmentation algorithms in broadleaf forests shows high accuracy only for canopy trees. International Journal of Applied Earth Observation and Geoinformation, 123, 103490.
Ball, J. G. C., Petrova, K., Coomes, D. A., & Flaxman, S. (2022). Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation. Methods in Ecology and Evolution, 13(11), 2622-2634.
Aubry-Kientz, M., Laybros, A., Weinstein, B., Ball, J. G. C., Jackson, T., Coomes, D., & Vincent, G. (2021). Multisensor data fusion for improved segmentation of individual tree crowns in dense tropical forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3927-3936.
Ball, J. G. C., Burgman, M. A., Goldman, E. D., & Lessmann, J. (2021). Protecting biodiversity and economic returns in resource‐rich tropical forests. Conservation Biology, 35(1), 263-273.
Vincent, G., Verley, P., Brede, B., Delaitre, G., Maurent, E., Ball, J. G. C., … & Barbier, N. (2023). Multi-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density. Remote Sensing of Environment, 286, 113442.
Ball, J. G. C., Reed, B. E., Grainger, R. G., Peters, D. M., Mather, T. A., & Pyle, D. M. (2015). Measurements of the complex refractive index of volcanic ash at 450, 546.7, and 650 nm. Journal of Geophysical Research: Atmospheres, 120(15), 7747-7757.
PhD Thesis
Understanding tropical forest dynamics through remote sensing and deep learning
Media
Unpicking the rhythms of the Amazon rainforest - Where I work: James Ball (2021), Nature. 591, 494
Languages
Technologies
Consultancy
I am available for consultancy work related to mapping forests and trees. My skills include utilizing advanced AI and computer vision techniques applied to remote sensing data for tropical forest tree detection and monitoring. My services can be beneficial for environmental consulting firms, governmental and non-governmental organizations, and businesses in sectors such as sustainable forestry, conservation, climate change mitigation, and ecological research. With an established track record in the application of cutting-edge technologies, I offer consultancy in project design, data analysis and interpretation, as well as the development and implementation of AI-based solutions tailored to your needs. If your organization requires support in these areas or has specific project needs, please feel free to contact me.
Prizes and awards
2022, Research Day Poster Presenter Award (Department of Plant Sciences, University of Cambridge)
2019, NERC PhD studentship
2017, AECOM Prize for outstanding overall academic performance (Imperial College London)
Supervision
- Thesis Title: “Improving urban tree management using high-resolution satellite data”
- Student Name: Andrés Zuñiga Gonzalez
- Course: MRes (AI4ER, Department of Earth Sciences)
- University/Institution: University of Cambridge
- Year: 2023
- Thesis Title: “Analysing changes in tropical tall trees with machine learning, LiDAR, and RGB imagery”
- Student Name: Sebastian Hickman
- Course: MRes (AI4ER, Department of Earth Sciences)
- University/Institution: University of Cambridge
- Year: 2021
- Thesis Title: “Forecasting deforestation using multispectral imagery time series via deep neural networks”
- Student Name: Katerina Petrova
- Course: M4R (Department of Mathematics)
- University/Institution: Imperial College London
- Year: 2019
Teaching
Undergraduate supervision: Responses to Global Change, Natural Sciences Tripos, Part II, University of Cambridge.
2023, Janet Moore Prize - nomination:
James has given clear and helpful supervisions and essay feedback. He’s instilled a confidence in my essay writing I have never had and I can’t stress how much that has affected my confidence going into my final exams. I feel that I am as well prepared as I could be for not just PL2 but other essay modules as well (e.g. ZM2 conservation science). His feedback focuses not just on the particular essay question but on essay writing technique for the module in general which has allowed me to improve in my essay writing each week despite changing between lecture series. With James’ supervision I have found myself enjoying writing essays each week! And the James’ supos always inspire conversations over new links within and between lecture series ideas.
Education
PhD in Plant Sciences (AI and Remote Sensing)
2019 - 2023
Department of Plant Sciences, University of Cambridge
MSc in Environmental Technology
2016 - 2017
Imperial College London
MPhys Physics
2009 - 2014
University of Oxford
