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Timnit Gebru

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NeurIPS Conference 2021 Conference Paper

Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa

  • Raesetje Sefala
  • Timnit Gebru
  • Nyalleng Moorosi
  • Luzango Mfupe
  • Richard Klein

Aerial images of neighborhoods in South Africa show the clear legacy of Apartheid, a former policy of political and economic discrimination against non-European groups, with completely segregated neighborhoods of townships next to gated wealthy areas. This paper introduces the first publicly available dataset to study the evolution of spatial apartheid, using 6, 768 high resolution satellite images of 9 provinces in South Africa. Our dataset was created using polygons demarcating land use, geographically labelled coordinates of buildings in South Africa, and high resolution satellite imagery covering the country from 2006-2017. We describe our iterative process to create this dataset, which includes pixel wise labels for 4 classes of neighborhoods: wealthy areas, non wealthy areas, non residential neighborhoods and vacant land. While datasets 7 times smaller than ours have cost over 1M to annotate, our dataset was created with highly constrained resources. We finally show examples of applications examining the evolution of neighborhoods in South Africa using our dataset.

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AAAI Conference 2017 Conference Paper

Fine-Grained Car Detection for Visual Census Estimation

  • Timnit Gebru
  • Jonathan Krause
  • Yilun Wang
  • Duyun Chen
  • Jia Deng
  • Li Fei-Fei

Targeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learningdriven approaches are cheaper and faster—with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classi- fied by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0. 82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighbourhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.

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