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arXiv · 2212.10735

NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters

Adiba Mahbub Proma·Md Saiful Islam·Stela Ciko·Raiyan Abdul Baten·Ehsan Hoque
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Abstract

Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning (ML) in this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing them according to the disaster management cycle. We compile a list of existing benchmark datasets introduced in the past five years. We propose a web platform - NADBenchmarks - where researchers can search for benchmark datasets for natural disasters, and we develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions for topics where they can contribute new benchmark datasets.

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Adiba Mahbub Proma, Md Saiful Islam, Stela Ciko, Raiyan Abdul Baten, Ehsan Hoque. 2022-12-21. NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters. https://arxiv.org/abs/2212.10735

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