Deforestation Drivers
Rank: 26th out of 108
Using satellite images to identify deforestation drivers to promote sustainable land use
About the competition
Detecting the drivers of deforestation is important to help reduce its harmful effects on forests. Understanding these drivers enables the development of smarter conservation strategies and sustainable land management practices.
The goal of this competition is to train an AI model to identify and segment the causes of deforestation using satellite images. The participants will work with labeled data showing land-use changes, such as farming expansion, mining, and urban growth, in tropical areas like the Amazon and Southeast Asia. The AI models developed will help understand the changing factors behind deforestation in different regions, enabling the development of precise and effective conservation strategies for better forest protection.
Solafune
The competition is organized by Solafune, they develop satellite and geospatial data technology. They create tools for industries like agriculture, disaster response, natural resource management, finance, defense, marketing, and insurance, helping tackle critical social and business challenges.
In addition to their technology, Solafune hosts data science competitions to foster collaboration and innovation. Their mission is to analyze global events and drive meaningful change using satellite technology.
Relevance
Deforestation is a global crisis, causing biodiversity loss, climate change, and water cycle disruptions. Addressing its root causes is crucial for protecting ecosystems, supporting communities, and fighting climate change.
This competition challenges participants to develop an AI model to track deforestation more accurately. The insights gained from the AI model will help shape future conservation strategies, drive reforestation efforts, and promote sustainable development.
Technical Details
For this competition 12-band data was obtained through the Sentinel-2 satellite. These bands included RGB-channels, but also information measured with larger wavelengths. The goal was to segment the data into four classes: Mining, Logging, Grassland/Shrubland and Plantation. The final prediction of the model converted predictions into polygons. The main challenge was building a model which would perform well considering the small training data set, consisting of only 176 annotated images.
Another grand challenge was the imbalances between classes. For example, the class logging did not appear in many training examples and usually had a very long and thin structure, unlike the classes plantation and grassland, which covered larger areas.
The main focuses were augmentations, model architectures and ensembling models. Multiple augmentations were tried to help the model generalize well. Different model architectures, such as UNet and Feature Pyramid Networks were used as they performed well on different classes. Lastly, we implemented multiple different ways of assembling the models, such as averaging their predictions, using certain models for certain classes and building a Ridge Regression model on top of our existing predictions.
UN Sustainable Development Goals
This competition contributes to the UN Sustainable Development Goal 13: Climate Action and Goal 15: Life on Land. By identifying the key drivers of deforestation, we are advancing efforts to protect, restore, and promote the sustainable use of terrestrial ecosystems.
