# QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1

arXiv: https://arxiv.org/abs/2403.18116 · HTML: https://mlsanity.com/paper/2403.18116/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [CaBuAr: California Burned Areas dataset for delineation](https://mlsanity.com/paper/2401.11519/) (2024)
- [CrisisViT: A Robust Vision Transformer for Crisis Image Classification](https://mlsanity.com/paper/2401.02838/) (2024)

## Built on by (most cited first, 8 of 8)

- [EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues](https://mlsanity.com/paper/2412.15190/) (2024)
- [GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks](https://mlsanity.com/paper/2411.19325/) (2024)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis](https://mlsanity.com/paper/2503.15625/) (2025)
- [Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking](https://mlsanity.com/paper/2407.18128/) (2024)
- [PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation](https://mlsanity.com/paper/2411.12636/) (2024)
- [CLOSP: A Unified Semantic Space for SAR, MSI, and Text in Remote Sensing](https://mlsanity.com/paper/2507.10403/) (2025)

## Abstract (arXiv)

Earthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to estimate damages and plan the actions needed for the restoration process. The use of seismic stations to monitor the strength and origin of earthquakes is limited when dealing with remote areas (we cannot have global capillary coverage). Identification and analysis of all affected areas is mandatory to support areas not monitored by traditional stations. Using social media images in crisis management has proven effective in various situations. However, they are still limited by the possibility of using communication infrastructures in case of an earthquake and by the presence of people in the area. Moreover, social media images and messages cannot be used to estimate the actual severity of earthquakes and their characteristics effectively. The employment of satellites to monitor changes around the globe grants the possibility of exploiting instrumentation that is not limited by the visible spectrum, the presence of land infrastructures, and people in the affected areas. In this work, we propose a new dataset composed of images taken from Sentinel-1 and a new series of tasks to help monitor earthquakes from a new detailed view. Coupled with the data, we provide a series of traditional machine learning and deep learning models as baselines to assess the effectiveness of ML-based models in earthquake analysis.