When top ML & AI researchers naively think they can improve social outcomes or social improvements! In this case it is Stefano Ermon of Stanford University! Lets' not forget that the brilliant physicist Albert Einstein was a stupid socialist!
This paper is most likely a joke! How easy is it for wealthy individuals to fool this model?
Will this kind of wealth modeling not be abused by greedy governments to tax the rich? The rich will vote with their feet etc. to avoid extra tax burdens.
From the abstract:
"Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone.
Satellite imagery offers global coverage and the possibility of predicting economic livelihoods at scale, yet existing approaches to predicting livelihoods with imagery or other non-traditional data often fail to reliably identify local-level variation and, as we show, degrade under temporal shift.
Here we introduce Tempov, a satellite foundation model pretrained by self-supervision on three million bi-temporal Landsat pairs and adapted with parameter-efficient fine-tuning to sparse survey labels. The model enables large-scale, high-resolution wealth mapping and dynamic measurement, including zero-shot nowcasting up to a decade after observed labels, retrospective hindcasting, and decadal change tracking, while outperforming existing neural network and geospatial foundation-model baselines.
In low-label regimes, Tempov achieves competitive accuracy with only 10% of survey samples, indicating substantially reduced dependence on expensive label collection.
The model further generalizes across populous countries within and outside Africa, and scales to a unified Africa-wide model with strong continent-level performance (, ), from which we generate high-resolution decadal maps of wealth and wealth changes for the African continent.
Analysis of these maps shows large variation in recent economic performance both within and across countries. Our open-source approach provides a pathway to timely, scalable, low-cost monitoring of wealth and poverty from routinely collected satellite data."
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