Novel Machine Learning Approach for Predicting Poverty using Temperature
and Remote Sensing Data in Ethiopia
- URL: http://arxiv.org/abs/2302.14835v1
- Date: Tue, 28 Feb 2023 18:32:16 GMT
- Title: Novel Machine Learning Approach for Predicting Poverty using Temperature
and Remote Sensing Data in Ethiopia
- Authors: Om Shah and Krti Tallam
- Abstract summary: A lack of poverty data prevents critical humanitarian organizations from responding to large-scale crises.
We propose a transfer learning model based on surface temperature change and remote sensing data to extract features useful for predicting poverty rates.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In many developing nations, a lack of poverty data prevents critical
humanitarian organizations from responding to large-scale crises. Currently,
socioeconomic surveys are the only method implemented on a large scale for
organizations and researchers to measure and track poverty. However, the
inability to collect survey data efficiently and inexpensively leads to
significant temporal gaps in poverty data; these gaps severely limit the
ability of organizational entities to address poverty at its root cause. We
propose a transfer learning model based on surface temperature change and
remote sensing data to extract features useful for predicting poverty rates.
Machine learning, supported by data sources of poverty indicators, has the
potential to estimate poverty rates accurately and within strict time
constraints. Higher temperatures, as a result of climate change, have caused
numerous agricultural obstacles, socioeconomic issues, and environmental
disruptions, trapping families in developing countries in cycles of poverty. To
find patterns of poverty relating to temperature that have the highest
influence on spatial poverty rates, we use remote sensing data. The two-step
transfer model predicts the temperature delta from high resolution satellite
imagery and then extracts image features useful for predicting poverty. The
resulting model achieved 80% accuracy on temperature prediction. This method
takes advantage of abundant satellite and temperature data to measure poverty
in a manner comparable to the existing survey methods and exceeds similar
models of poverty prediction.
Related papers
- Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach.
Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z) - Fairness and representation in satellite-based poverty maps: Evidence of
urban-rural disparities and their impacts on downstream policy [5.456665139074406]
This paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines.
Our findings highlight the importance of careful error and bias analysis before using satellite-based poverty maps in real-world policy decisions.
arXiv Detail & Related papers (2023-05-02T21:07:35Z) - Learning and Reasoning Multifaceted and Longitudinal Data for Poverty
Estimates and Livelihood Capabilities of Lagged Regions in Rural India [22.98110639419913]
The project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators.
The project also intends to examine causation and longitudinal analysis to examine the reasons for poverty.
arXiv Detail & Related papers (2023-04-27T05:33:08Z) - Interpreting wealth distribution via poverty map inference using
multimodal data [0.0]
We propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple populated places.
These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media.
Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them.
arXiv Detail & Related papers (2023-02-17T11:35:44Z) - Graph-based Village Level Poverty Identification [52.12744462605759]
The development of the Web infrastructure and its modeling tools provides fresh approaches to identifying poor villages.
By modeling the village connections as a graph through the geographic distance, we show the correlation between village poverty status and its graph topological position.
We propose the first graph-based method to identify poor villages.
arXiv Detail & Related papers (2023-02-14T06:58:40Z) - Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery.
Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
arXiv Detail & Related papers (2023-01-04T05:19:33Z) - Predicting Poverty Level from Satellite Imagery using Deep Neural
Networks [0.0]
I develop deep learning computer vision methods that can predict a region's poverty level from an overhead satellite image.
I explore the impact that data quantity and data augmentation have on the representational power and overall accuracy of the networks.
arXiv Detail & Related papers (2021-11-30T18:57:24Z) - Interpretable Poverty Mapping using Social Media Data, Satellite Images,
and Geospatial Information [0.0]
We present a interpretable and cost-efficient approach to poverty estimation using machine learning and readily accessible data sources.
We achieve an $R2$ of 0.66 for wealth estimation in the Philippines, compared to 0.63 using satellite imagery.
arXiv Detail & Related papers (2020-11-27T05:24:53Z) - Predicting Livelihood Indicators from Community-Generated Street-Level
Imagery [70.5081240396352]
We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery.
By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
arXiv Detail & Related papers (2020-06-15T18:12:12Z) - Generating Interpretable Poverty Maps using Object Detection in
Satellite Images [80.35540308137043]
We demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to satellite images.
Using the weighted counts of objects as features, we achieve 0.539 Pearson's r2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks.
arXiv Detail & Related papers (2020-02-05T02:50:01Z) - Improving a State-of-the-Art Heuristic for the Minimum Latency Problem
with Data Mining [69.00394670035747]
Hybrid metaheuristics have become a trend in operations research.
A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques.
arXiv Detail & Related papers (2019-08-28T13:12:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.