Seeing poverty from space, how much can it be tuned?

07/30/2021
by   Tomas Sako, et al.
32

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 5

page 6

page 7

page 9

page 10

page 13

page 14

07/05/2019

AI-based evaluation of the SDGs: The case of crop detection with earth observation data

The framework of the seventeen sustainable development goals is a challe...
01/21/2022

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observatio...
07/02/2020

Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning

This work explores the combination of free cloud computing, free open-so...
12/21/2021

Deep Learning and Earth Observation to Support the Sustainable Development Goals

The synergistic combination of deep learning models and Earth observatio...
06/12/2020

Predicting cell phone adoption metrics using satellite imagery

Approximately half of the global population does not have access to the ...
04/13/2020

Distributed Resources for the Earth System Grid Advanced Management (DREAM)

The DREAM project was funded more than 3 years ago to design and impleme...
08/03/2021

Predicting Zip Code-Level Vaccine Hesitancy in US Metropolitan Areas Using Machine Learning Models on Public Tweets

Although the recent rise and uptake of COVID-19 vaccines in the United S...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.