From the polar bear capital of the world to tropical Gabon, AI is helping to fight biodiversity loss
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Our team has gathered significant expertise in interactive data visualization, machine learning, and developing artificial intelligence solutions. We’ve decided to put these technologies to work in resolving the world’s most pressing challenges.
In our Data for Good initiative, we focus primarily on climate change and environmental protection projects, because we believe these issues will become the primary challenges to humanity in the decades to come.
We offer many of our services at significantly reduced rates or pro-bono, when the goal of the project is aligned with our Data for Good mission.
In areas that need the most innovation to limit the GHG emissions, we identified four from which we believe Machine Learning could speed up the break-through process.
Vegetable, cellular meat, and dairy products
Crops resistant to drought and floods
We are committed to supporting scientists and organizations working on real business solutions limiting emissions. In the case of high-impact climate change projects, especially those aimed at reducing carbon emissions, we will provide our services pro-bono.
We are convinced that a combination of our technical skills with the domain knowledge of our partner organizations will result in innovative solutions for the plant.
If you are a public sector institution, an NGO, an academic institution, or a public benefit corporation working on social good projects, we would like to hear from you.
Appsilon’s Data for Good efforts have been widely recognized in international media. Our Mbaza AI computer vision project, created in collaboration with the University of Stirling and Parcs Gabon to help preserve animal populations in Africa, was featured in Euronews, The Independent, and other notable outlets.
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Camera trap imaging (automatic photography of animal species in the wild) is becoming the gold standard in biodiversity conservation. It allows for accurately monitoring large swaths of land at an unprecedented scale. With ML and Computer Vision we tapped the potential of automated intelligence to improve reaction time for conservation efforts.
Partnering with dendrologists, we studied the life cycle stages of plants from Polish forests. Utilizing citizen science platforms allows for large-scale, high precision monitoring of the shifts in ecosystems’ synchrony as it is affected by climate. Read more
Mbaza is used in the national parks of Gabon to help protect and monitor their pristine ecosystems. It is powered by image classification models, allowing the ecologists and park rangers to tap into insights from the data collected by camera traps. Using their existing equipment, they can process the data within a day. Without the tool, this typically took a month. Read more
The vitality of plankton is a crucial window into the health of the Arctic ocean. We have developed methods of assessing the nutritive value of plankton using Image Segmentation, which opens the possibility of monitoring the Arctic oceans’ ecosystem on a large scale. This is particularly important in the era of accelerated shifts caused by the climate crisis. Publication coming soon
Climate change will exacerbate the consequences of natural disasters. Developing countries will be hit the hardest. We helped Dr. Junko Mochizuki build a decision support tool for policymakers in Madagascar to better mitigate this risk.
Actions taken and lessons learned at a 24-hour hackathon dealing with fly-tipping problems.
As part of our D4G Initiative and with the support of a Google grant, Appsilon Data Science will be contributing to to the work of biodiversity conservationists at the National Parks Agency in Gabon in collaboration with the University of Stirling.
At Appsilon Data Science Consulting we advocate an exploratory approach towards utilizing machine learning to address climate change. We seek to inspire stakeholders fighting climate change by showcasing technologies<br />
This is the first of two blog posts about our recent participation in the Pandemic Response Hackathon. Our project (CoronaRank) was one of only 5 projects out of 230 submissions chosen to present at the closing ceremony.