The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth.
Content Editors rate, curate and regularly update what we believe are the top 11% of all AI resource and good practice examples and is why our content is rated from 90% to 100%. Content rated less than 90% is excluded from this site. All inclusions are vetted by experienced professionals with graduate level data science degrees.
In the broadest sense, any inclusion of content on this site is not an endorsement or recommendation of any service, product or content that may be discussed, recommended, endorsed or affiliated with the content, company or spokesperson. We are a 501(c)3 nonprofit and receive no website advertising monies or direct or indirect compensation for any content or other information on any of our websites. For more information, visit our TOS.