SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning

• URL: http://arxiv.org/abs/2111.04724v1
• Date: Mon, 8 Nov 2021 18:59:04 GMT
• Title: SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
• Authors: Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B. Lobell, Stefano Ermon
• Abstract summary: Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
• Score: 63.192289553021816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Progress toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically have come from ground surveys with sparse temporal and spatial coverage. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media, to provide insights into progress toward SDGs. Despite promising early results, approaches to using such data for SDG measurement thus far have largely evaluated on different datasets or used inconsistent evaluation metrics, making it hard to understand whether performance is improving and where additional research would be most fruitful. Furthermore, processing satellite and ground survey data requires domain knowledge that many in the machine learning community lack. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs, including tasks related to economic development, agriculture, health, education, water and sanitation, climate action, and life on land. Datasets for 11 of the 15 tasks are released publicly for the first time. Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.

Related papers

• Sustainable Visions: Unsupervised Machine Learning Insights on Global Development Goals [0.3764231189632788]
  The United Nations 2030 Agenda for Sustainable Development outlines 17 goals to address global challenges. Progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors. No country on track to achieve all goals by 2030.
  arXiv  Detail & Related papers  (2024-09-19T03:10:49Z)
• Leveraging Artificial Intelligence Technology for Mapping Research to Sustainable Development Goals: A Case Study [6.551575555269426]
  This study employed over 82,000 publications from an Australian university as a case study. We utilized a similarity measure to map these publications onto Sustainable Development Goals. We leveraged the OpenAI GPT model to conduct the same task, facilitating a comparative analysis between the two approaches.
  arXiv  Detail & Related papers  (2023-11-09T11:44:22Z)
• Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring [2.4107880640624706]
  We describe a data-driven system that seeks to automate the process of creating an Sustainable Development Goals framework. We propose a novel method for collecting and filtering a dataset of texts from different web sources and a knowledge graph relevant to a set of companies. Our results indicate that our best performing model can accurately predict SDG scores with a micro average F1 score of 0.89.
  arXiv  Detail & Related papers  (2023-08-04T15:14:16Z)
• GEO-Bench: Toward Foundation Models for Earth Monitoring [139.77907168809085]
  We propose a benchmark comprised of six classification and six segmentation tasks. This benchmark will be a driver of progress across a variety of Earth monitoring tasks.
  arXiv  Detail & Related papers  (2023-06-06T16:16:05Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control [55.41644538483948]
  High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research. As this technology advances, it is becoming increasingly useful also in society. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
  arXiv  Detail & Related papers  (2021-08-26T11:45:26Z)
• Seeing poverty from space, how much can it be tuned? [0.0]
  We demonstrate that individuals with no organizational affiliation can participate in the improvement of predicting local poverty levels in a given agro-ecological environment. The approach builds upon several pioneering efforts related to mapping poverty by deep learning to process satellite imagery and "ground-truth" data from the field. 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.
  arXiv  Detail & Related papers  (2021-07-30T15:23:54Z)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)
• Using satellite imagery to understand and promote sustainable development [87.72561825617062]
  We synthesize the growing literature that uses satellite imagery to understand sustainable development outcomes. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution of satellite imagery. We review recent machine learning approaches to model-building in the context of scarce and noisy training data.
  arXiv  Detail & Related papers  (2020-09-23T05:20:00Z)

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.