Team BLTV - NepTune
You can view our submitted presentation here.
Kaggle: https://www.kaggle.com/competitions/bizinnovate-2023/overview
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.
The goal of this competition was to gain insight toward the SDG of clean water. The challenge was to estimate the water quality index of a given region using satellite images. The data will provide the water quality index of households, which is ranked on a 1-5 scale. Five is the “highest quality”, while one is the lowest.
This dataset and the motivation behind it is based on the work of SustainBench, where more details can be found [1].
C. Yeh, C. Meng, S. Wang, A. Driscoll, E. Rozi, P. Liu, J. Lee, M. Burke, D. Lobell, and S. Ermon, “SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning,” in Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (Round 2), Dec. 2021. [Online]. Available: https://openreview.net/forum?id=5HR3vCylqD.
Shield:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
