Using Machine Learning to Combat Deforestation

How an organization is using old cell phones and machine learning to fight illegal logging

Topher White explains how Rainforest Connection is using TensorFlow to combat illegal logging

Using Machine Learning to Combat Deforestation

Every year, tropical forest area the size of Austria is lost to illegal logging[1]. The sheer size of forests and use of stealth tactics by loggers make it hard for local guardians to intercept illegal logging until it’s too late. Organizations like Rainforest Connection are using remote sensing technologies to monitor forests and detect illegal logging in real time[1]. They work with forest communities to set up modified old cell phones fitted with solar panels in trees to monitor the forest[3]. These acoustic monitoring systems process a continuous stream of audio data from the forest. Using extremely basic algorithms, they detect distinct sounds related to illegal activity such as chainsaws and/or logging trucks. When this happens, real time warnings are emitted to patrol members enabling them to intercept and stop illegal logging activities.

In application, acoustic monitoring devices have run up against limitations related to processing power and human capability. Consequently, Rainforest Connection is looking to machine learning to better use its data. First, each cellphone’s processing power limits its capacity to only basic algorithms. While these have proven effective in identifying distinct sounds such as a chainsaw, they cannot detect more sophisticated sounds such as human voices. This limits their monitoring capabilities[4]. Second, the lack of programming capabilities restricts the ability of remote forest communities to improve monitoring algorithms and predictive models as increased data is received. Third, these constraints limit the potential applications of this large collection of forest audio data for other purposes, such as research. Given that Rainforest Connection is a non-profit, such other applications could provide sources for funding that would enable it to continue, and even expand its conservation work.

To address these issues, Rainforest Connection has partnered with Google’s TensorFlow open source machine learning platform. Processing data in the cloud solves the lack of human capability on the ground, since algorithms can be remotely adjusted. Moreover, the higher processing power of the TensorFlow framework can compensate for the lack of processing power in the devices, enabling detection of softer audio inputs (human voices) and improvement of detection quality. Furthermore, pattern recognition within TensorFlow can provide predictive insight into the behavior of illegal loggers, such as what areas they are most likely to target. Finally, TensorFlow is enabling wider applications of the data, such as using bird and animal sounds to detect the presence and movement patterns of endangered species like the Jaguar in the Amazon and improve conservation models[5].

The efficacy of the Rainforest Connection model can be further enhanced in two ways. First, by partnering with local universities and research centers, Rainforest Connection can add more regional capacity to work with forest communities. Moreover, enabling additional researchers to use data captured by the devices would increase the variety of machine learning tools and algorithms developed which could improve the overall predictive performance[6]. Second, empowering the local forest community to use technology as a tool to facilitate conservation is essential. Rainforest Connection can focus on developing graphically focused interfaces to enable the community to use machine learning outputs more independently rather than permanently relying on Rainforest Connection programmers as a go-between.

As Rainforest Connection continues on this venture, two open questions remain. First, how can Rainforest Connection use machine learning to enhance the learnings from the data while keeping the basic model at a level that is accessible and understandable to the forest communities it serves? Second, is there a role for distributed innovation here, whereby regular citizens can work with the data in ways that might be helpful? Citizen science programs have been successful in developing species maps and gathering critical conservation data in the past[7] (799 words).

[1] Centre for Global Development book, Why Forests? Why now? The Science, Economics and Politics of Tropical Forests and Climate Change (December 2016), by Frances Seymour and Jonah Busch

[2] White, Topher, “The fight against illegal deforestation with TensorFlow,” Google Blog (blog), 21 March 2018, accessed 12 November 2018

[4] Coldewey, Devin, “ Rainforest Connection enlists machine learning to listen for loggers and jaguars in the Amazon,” TechCrunch, 23 March 2018, accessed 12 November 12, 2018.

[5] Kelling, S., Lagoze, C., Wong, W., Yu, J., Damoulas, T., Gerbracht, J., . . . Gomes, C. (2013). eBird: A human / computer learning network to improve biodiversity conservation and research. AI Magazine, 34(1), 10-20. Retrieved from

[6] Balmford, A., and K. J. Gaston. 1999. Why biodiversity surveys are good value. Nature 398:204–205.

[7] Moran, Niklas ; Nieland, Simon ; Tintrup Gen. Suntrup, Gregor ; Kleinschmit, Birgit International Journal of Applied Earth Observations and Geoinformation, February 2017, Vol.54, pp.124-133



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3 thoughts on “Using Machine Learning to Combat Deforestation

  1. I think the role of distributed innovation is high here. This appears to be a robust and unique data set. One topic that comes immediately to mind is the role of this data set in noise pollution research. Can this dataset be leveraged by citizens of the community to assess human noise and its impact on animals in an area? I hope so!

  2. The opportunity of citizen science programs is really interesting here. Equipped with the monitoring infrastructure and analytics platform, the local community could be mapping and tracking illegal logging activities or attempts to not only ensure the activity is stopped but to identify and bring to justice the people and/greater network that is involved. I’m curious what other sound data could be picked up and logged by this program. I agree that working together with local universities and researchers would put additional man and brain power behind this effort to add to its impact.

  3. This is such an interesting concept that I wasn’t aware of before. I’m curious as to the methods employed before acoustic monitoring and why they were so ineffective. For example, I thought perhaps satellite imaging could have been an option, but I’m assuming that there is a time lag concern there – by the time deforestation is noticed on a satellite image, it’s too late. I think it’s interesting to think about how Rainforest Connection, as a non-profit, literally can’t afford human capital. Many for-profit companies find it cheaper to invest in growing their human workforce BEFORE transitioning to machines/software – Rainforest Connection doesn’t appear to have this “luxury” which I think could ultimately be a win for them in the sense that they don’t have to deal with the difficult conversation around human capital redistribution and the ethics around this “future of work”.

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