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Every minute, the world loses an area of forest the size of 48 football fields. And deforestation in the Amazon Basin accounts for the largest share, contributing to reduced biodiversity, habitat loss, climate change, and other devastating effects. But better data about the location of deforestation and human encroachment on forests can help governments and local stakeholders respond more quickly and effectively. Planet, designer and builder of the world’s largest constellation of Earth-imaging satellites, will soon be collecting daily imagery of the entire land surface of the earth at 3-5 meter resolution. While considerable research has been devoted to tracking changes in forests, it typically depends on coarse-resolution imagery from Landsat (30 meter pixels) or MODIS (250 meter pixels). This limits its effectiveness in areas where small-scale deforestation or forest degradation dominate. Furthermore, these existing methods generally cannot differentiate between human causes of forest loss and natural causes. Higher resolution imagery has already been shown to be exceptionally good at this, but robust methods have not yet been developed for Planet imagery. In this competition, Planet and its Brazilian partner SCCON are challenging Kagglers to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world - and ultimately how to respond.tunisia-fraud-detection
Tax fraud is the intentional act of lying on a tax return form with the intent to lower one’s tax liability. Under-reporting is one of the most common types of tax frauds. It consists of filing a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment in much-needed services. The objective of the challenge is to detect tax fraud. This is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Using historical data, a supervised machine learning technique that detects potential fraudulent taxpayers will increase the operational efficiency of the tax supervision process.zindiweekendz-learning-south-african-covid-19-vulnerability-map
Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date. This challenge is to develop a proof-of-concept for how machine learning can help governments more accurately map COVID-19 risk in 2020 using old data, without requiring a new costly, risky, and time-consuming on-the-ground survey. The 2011 census gives us valuable information for determining who might be most vulnerable to COVID-19 in South Africa. However, the data is nearly 10 years old, and we expect that some key indicators will have changed in that time. Building an up-to-date map showing where the most vulnerable are located will be a key step in responding to the disease. A mapping effort like this requires bringing together many different inputs and tools. For this competition, we’re starting small. Can we infer important risk factors from more readily available data? The task is to predict the percentage of households that fall into a particularly vulnerable bracket - large households who must leave their homes to fetch water - using 2011 South African census data. Solving this challenge will show that with machine learning it is possible to use easy-to-measure stats to identify areas most at risk even in years when census data is not collected.Love Open Source and this site? Check out how you can help us