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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.finatial-inclusion-in-africa
Financial Inclusion remains one of the main obstacles to economic and human development in Africa. For example, across Kenya, Rwanda, Tanzania, and Uganda only 9.1 million adults (or 13.9% of the adult population) have access to or use a commercial bank account. Traditionally, access to bank accounts has been regarded as an indicator of financial inclusion. Despite the proliferation of mobile money in Africa, and the growth of innovative fintech solutions, banks still play a pivotal role in facilitating access to financial services. Access to bank accounts enable households to save and facilitate payments while also helping businesses build up their credit-worthiness and improve their access to other finance services. Therefore, access to bank accounts is an essential contributor to long-term economic growth. The objective of this competition is to create a machine learning model to predict which individuals are most likely to have or use a bank account. The models and solutions developed can provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania and Uganda, while providing insights into some of the key demographic factors that might drive individuals’ financial outcomes.Love Open Source and this site? Check out how you can help us