Deep Geospatial Interpolation Networks

• URL: http://arxiv.org/abs/2108.06670v1
• Date: Sun, 15 Aug 2021 06:57:36 GMT
• Title: Deep Geospatial Interpolation Networks
• Authors: Sumit Kumar Varshney, Jeetu Kumar, Aditya Tiwari, Rishabh Singh, Venkata M. V. Gunturi, and Narayanan C. Krishnan
• Abstract summary: We propose a novel deep neural network called as Deep Geospatial Interpolation Network(DGIN) DGIN incorporates both spatial and temporal relationships and has significantly lower training time. We evaluate DGIN on the MODIS dataset from two different regions.
• Score: 15.942343748489376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpolation in Spatio-temporal data has applications in various domains such as climate, transportation, and mining. Spatio-Temporal interpolation is highly challenging due to the complex spatial and temporal relationships. However, traditional techniques such as Kriging suffer from high running time and poor performance on data that exhibit high variance across space and time dimensions. To this end, we propose a novel deep neural network called as Deep Geospatial Interpolation Network(DGIN), which incorporates both spatial and temporal relationships and has significantly lower training time. DGIN consists of three major components: Spatial Encoder to capture the spatial dependencies, Sequential module to incorporate the temporal dynamics, and an Attention block to learn the importance of the temporal neighborhood around the gap. We evaluate DGIN on the MODIS reflectance dataset from two different regions. Our experimental results indicate that DGIN has two advantages: (a) it outperforms alternative approaches (has lower MSE with p-value < 0.01) and, (b) it has significantly low execution time than Kriging.

Related papers

• STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring [13.472378581383628]
  This paper proposes an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring. Three layers of temporal dilated causal convolutional network are used to capture long-range temporal correlations. The model integrates the gated-activated original, local synchronous joint spatial-temporal, and long-range temporal features to achieve comprehensive prediction.
  arXiv  Detail & Related papers  (2025-04-10T18:32:56Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• 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)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting [7.232141271583618]
  We propose a novel deep learning model for traffic forecasting named inefficient-Context Spatio-Temporal Joint Linear Attention (SSTLA) SSTLA applies linear attention to a joint graph to capture global dependence between alltemporal- nodes efficiently. Experiments on two real-world traffic datasets, England and Temporal7, demonstrate that our STJLA can achieve 9.83% and 3.08% 3.08% accuracy in MAE measure over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-12-04T06:39:18Z)
• Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [39.65520262751766]
  We develop a new traffic prediction framework-Spatial-Temporal Graph Diffusion Network (ST-GDN) In particular, ST-GDN is a hierarchically structured graph neural architecture which learns not only the local region-wise geographical dependencies, but also the spatial semantics from a global perspective. Experiments on several real-life traffic datasets demonstrate that ST-GDN outperforms different types of state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-10-08T11:19:06Z)
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting [22.421667339552467]
  Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. Existing works typically utilize shallow graph convolution networks (GNNs) and temporal extracting modules to model spatial and temporal dependencies respectively. We propose Spatial-Temporal Graph Ordinary Differential Equation Networks (STGODE), which captures spatial-temporal dynamics through a tensor-based ordinary differential equation (ODE) We evaluate our model on multiple real-world traffic datasets and superior performance is achieved over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-06-24T11:48:45Z)
• SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network [2.524966118517392]
  We have designed a simplified S-temporal GNN(SST-GNN) that effectively encodes the dependency by separately aggregating different neighborhood. We have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets.
  arXiv  Detail & Related papers  (2021-03-31T18:28:44Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• 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.