NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters
This work provides a resource for ML researchers working on natural disaster management, but it is incremental as it compiles existing datasets rather than creating new ones.
The paper addresses the lack of benchmark datasets for machine learning tasks related to natural disasters by compiling existing datasets and proposing a web platform called NADBenchmarks to help researchers find and use them, aiming to accelerate progress in this field.
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.