  Since the United Nations launched the Sustainable Development Goals (SDG) in
2015, numerous universities, NGOs and other organizations have attempted to
develop tools for monitoring worldwide progress in achieving them. Led by
advancements in the fields of earth observation techniques, data sciences and
the emergence of artificial intelligence, a number of research teams have
developed innovative tools for highlighting areas of vulnerability and tracking
the implementation of SDG targets. In this paper we demonstrate that
individuals with no organizational affiliation and equipped only with common
hardware, publicly available datasets and cloud-based computing services can
participate in the improvement of predicting machine-learning-based approaches
to predicting local poverty levels in a given agro-ecological environment. The
approach builds upon several pioneering efforts over the last five years
related to mapping poverty by deep learning to process satellite imagery and
"ground-truth" data from the field to link features with incidence of poverty
in a particular context. The approach employs new methods for object
identification in order to optimize the modeled results and achieve
significantly high accuracy. A key goal of the project was to intentionally
keep costs as low as possible - by using freely available resources - so that
citizen scientists, students and organizations could replicate the method in
other areas of interest. Moreover, for simplicity, the input data used were
derived from just a handful of sources (involving only earth observation and
population headcounts). The results of the project could therefore certainly be
strengthened further through the integration of proprietary data from social
networks, mobile phone providers, and other sources.
