SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
- URL: http://arxiv.org/abs/2311.15530v1
- Date: Mon, 27 Nov 2023 04:23:47 GMT
- Title: SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
- Authors: Jia Li, Yanyan Shen, Lei Chen, Charles Wang Wai NG
- Abstract summary: We propose a data-driven self-supervised learning framework for rainfall spatial analysis.
By mining latent spatial patterns from historical data, SpaFormer can learn informative embeddings for raw data and then adaptively model spatial correlations.
Our method outperforms the state-of-the-art solutions in experiments on two real-world raingauge datasets.
- Score: 37.212272184144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The acquisition of accurate rainfall distribution in space is an important
task in hydrological analysis and natural disaster pre-warning. However, it is
impossible to install rain gauges on every corner. Spatial interpolation is a
common way to infer rainfall distribution based on available raingauge data.
However, the existing works rely on some unrealistic pre-settings to capture
spatial correlations, which limits their performance in real scenarios. To
tackle this issue, we propose the SSIN, which is a novel data-driven
self-supervised learning framework for rainfall spatial interpolation by mining
latent spatial patterns from historical observation data. Inspired by the Cloze
task and BERT, we fully consider the characteristics of spatial interpolation
and design the SpaFormer model based on the Transformer architecture as the
core of SSIN. Our main idea is: by constructing rich self-supervision signals
via random masking, SpaFormer can learn informative embeddings for raw data and
then adaptively model spatial correlations based on rainfall spatial context.
Extensive experiments on two real-world raingauge datasets show that our method
outperforms the state-of-the-art solutions. In addition, we take traffic
spatial interpolation as another use case to further explore the performance of
our method, and SpaFormer achieves the best performance on one large real-world
traffic dataset, which further confirms the effectiveness and generality of our
method.
Related papers
- OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries.
OPUS incorporates a suite of non-trivial strategies to enhance model performance.
Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
arXiv Detail & Related papers (2024-09-14T07:44:22Z) - SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
Weather forecasting plays a critical role in various sectors, driving decision-making and risk management.
Traditional methods often struggle to capture the complex dynamics of meteorological systems.
We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
arXiv Detail & Related papers (2024-05-29T08:00:15Z) - Deep Generative Data Assimilation in Multimodal Setting [0.1052166918701117]
In this work, we propose SLAMS: Score-based Latent Assimilation in Multimodal Setting.
We assimilate in-situ weather station data and ex-situ satellite imagery to calibrate the vertical temperature profiles, globally.
Our work is the first to apply deep generative framework for multimodal data assimilation using real-world datasets.
arXiv Detail & Related papers (2024-04-10T00:25:09Z) - Foundation Models for Generalist Geospatial Artificial Intelligence [3.7002058945990415]
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive data.
We have utilized this framework to create Prithvi, a transformer-based foundational model pre-trained on more than 1TB of multispectral satellite imagery.
arXiv Detail & Related papers (2023-10-28T10:19:55Z) - SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation [8.636014676778682]
Open data is frequently released spatially aggregated, usually to comply with privacy policies. But coarse, heterogeneous aggregations complicate coherent learning and integration for downstream AI/ML systems.
We propose an overarching model named Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention layers into the Gated Recurrent Unit (GRU) model.
For scenarios with limited historical training data, we show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples.
arXiv Detail & Related papers (2023-06-09T21:01:29Z) - One-Shot Domain Adaptive and Generalizable Semantic Segmentation with
Class-Aware Cross-Domain Transformers [96.51828911883456]
Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data.
Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation.
We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
arXiv Detail & Related papers (2022-12-14T15:54:15Z) - Unraveled Multilevel Transformation Networks for Predicting
Sparsely-Observed Spatiotemporal Dynamics [12.627823168264209]
We propose a model that learns to predict unknown dynamics using data from sparsely-distributed data sites.
We demonstrate the advantage of our approach using both synthetic and real-world climate data.
arXiv Detail & Related papers (2022-03-16T14:44:05Z) - Cyclic Graph Attentive Match Encoder (CGAME): A Novel Neural Network For
OD Estimation [8.398623478484248]
Origin-Destination Estimation plays an important role in traffic management and traffic simulation in the era of Intelligent Transportation System (ITS)
Previous model-based models face the under-determined challenge, thus desperate demand for additional assumptions and extra data exists.
We propose Cyclic Graph Attentive Matching (C-GAME) based on a novel Graph Matcher with double-layer attention mechanism.
arXiv Detail & Related papers (2021-11-26T08:57:21Z) - ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework
for LiDAR Point Cloud Segmentation [111.56730703473411]
Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations.
Simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels.
ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning.
arXiv Detail & Related papers (2020-09-07T23:46:08Z) - A Spatial-Temporal Attentive Network with Spatial Continuity for
Trajectory Prediction [74.00750936752418]
We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC)
First, spatial-temporal attention mechanism is presented to explore the most useful and important information.
Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
arXiv Detail & Related papers (2020-03-13T04:35:50Z)
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.