Accurate and reliable crop yield estimation under climate stress conditions is essential to ensure global food security. The crop yield variation can be characterized by the daily available climate factors and captured by the multi-temporal remote sensing observations. However, the fusion of multi-temporal remote sensing and meteorological data with different temporal resolutions remains insufficient for crop yield estimation. In this paper, we introduce a two-stream model to perform a feature-level fusion of the vegetation indices and climate variables across multiple time scales. Each stream builds upon a pyramid structure that progressively aggregates the input time series into multi-time-scale feature embeddings. The proposed model is tested in the nine Midwestern states within the US Corn Belt at county level over the years 2006-2012. Quantitative results show that the two-stream model outperforms the one-stream model and typical machine learning under climate stress. The higher estimation performance indicates that feature-level fusion is superior to utilizing the multi-source data rather than simply stacking the input data. Spatial visualization demonstrates that our approach effectively learns to capture the spatial pattern of yield loss, particularly in extreme heat and drought condition. Expanding the number of streams within the proposed architecture enables fusing more data sources, holding promise for crop yield modeling at larger scales.

Synthetic aperture radar (SAR) can be used to obtain remote sensing images of different growth stages of crops under all weather conditions. Such time-series SAR images can provide an abundance of temporal and spatial features for use in large-scale crop mapping and analysis. In this study, we propose a temporal feature-based segmentation (TFBS) model for accurate crop mapping using time-series SAR images. This model first extracts deep-seated temporal features and then learns the spatial context of the extracted temporal features for crop mapping. The results indicate that the TFBS model significantly outperforms traditional long short-term memory (LSTM), U-network, and convolutional LSTM models in crop mapping based on time-series SAR images. TFBS demonstrates better generalizability than other models in the study area, which makes it more transferable, and the results show that data augmentation can significantly improve this generalizability. The visualization of the temporal features extracted by the TFBS shows that there is a high degree of intraclass homogeneity among rice fields and interclass heterogeneity between rice fields and other features. TFBS also achieved the highest accuracy of the four deep learning models for multicrop classification in the study area. This study presents a feasible way of producing high-accuracy large-scale crop maps based on the proposed model.

The traffic speed information of an urban road network is generally estimated using the widely available taxi GPS data. However, taxi usages are preponderantly restricted to areas with high population density, which results in limited spatial coverage of collected taxi GPS data. Moreover, the traffic speeds of taxies are not guaranteed to well represent the traffic speeds of other types of vehicles. In this study, we address these issues by introducing an infinite Gaussian mixture model to estimate traffic speed distribution. The variational inference method is employed to deal with the complicated parameter estimation problem. The proposed mixture model simultaneously combines taxi GPS data, bus GPS data, and mobile phone GPS data, which not only generates the mixed traffic-speed distribution of different types of vehicles but also improves the spatial coverage and the quality of traffic speed estimation. Surprisingly, we find that the incorporation of mobile phone GPS data can considerably improve the model’s ability to sense anomalous traffic conditions. Finally, the mixed traffic-speed distribution is validated using the license plate recognition data.

While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic forecasting. However, existing studies seldom consider external factors or neglect the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations. Since knowledge graphs and traffic networks are essentially heterogeneous networks, it is challenging to integrate the information in both networks. On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network. Experimental results on the real-world dataset show that our strategy enhances the forecasting performances of backbones at various prediction horizons. The ablation and perturbation analysis further verify the effectiveness and robustness of the proposed method. To the best of our knowledge, this is the first study that constructs and utilizes a knowledge graph to facilitate traffic forecasting; it also offers a promising direction to integrate external information and spatial-temporal information for traffic forecasting. The source code is available athttps://github.com/lehaifeng/T-GCN/tree/master/KST-GCN.

Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in real-world applications. However, artificial neural networks face the well-known problem of catastrophic forgetting. What is worse, the degradation of previously learned skills becomes more severe as the task sequence increases, known as the long-term catastrophic forgetting. It is due to two facts: first, as the model learns more tasks, the intersection of the low-error parameter subspace satisfying for these tasks becomes smaller or even does not exist; second, when the model learns a new task, the cumulative error keeps increasing as the model tries to protect the parameter configuration of previous tasks from interference. Inspired by the memory consolidation mechanism in mammalian brains with synaptic plasticity, we propose a confrontation mechanism in which Adversarial Neural Pruning and synaptic Consolidation (ANPyC) is used to overcome the long-term catastrophic forgetting issue. The neural pruning acts as long-term depression to prune task-irrelevant parameters, while the novel synaptic consolidation acts as long-term potentiation to strengthen task-relevant parameters. During the training, this confrontation achieves a balance in that only crucial parameters remain, and non-significant parameters are freed to learn subsequent tasks. ANPyC avoids forgetting important information and makes the model efficient to learn a large number of tasks. Specifically, the neural pruning iteratively relaxes the current task's parameter conditions to expand the common parameter subspace of the task; the synaptic consolidation strategy, which consists of a structure-aware parameter-importance measurement and an element-wise parameter updating strategy, decreases the cumulative error when learning new tasks. Our approach encourages the synapse to be sparse and polarized, which enables long-term learning and memory. ANPyC exhibits effectiveness and generalization on both image classification and generation tasks with multiple layer perceptron, convolutional neural networks, and generative adversarial networks, and variational autoencoder. The full source code is available at https://github.com/GeoX-Lab/ANPyC.

Traffic flow data collected by traffic sensing devices is crucially important for transportation planning and transportation management. However, traffic sensing devices are typically distributed sparsely in road networks owing to their high installation and maintenance costs. The present study combines license plate recognition (LPR) data with taxi GPS trajectory data to develop a data-driven approach for estimating traffic flow in large road networks. The approach is applied to estimate traffic flow for an actual road network comprising 5,495 road segments using the traffic flow records of only 68 road segments (1.2% of the total). Five-fold cross validation is employed to verify the estimated traffic flow, and the data requirements for implementing the proposed method are analyzed. The developed data-driven approach provides an alternative and cost-efficient way of acquiring additional traffic flow information rather than installing more traffic sensing devices on roads.

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN.

Studying human movement citywide is important for understanding mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extracted from mobile phone data. We design a suite of visualization techniques to illustrate the dynamic evolutions of the flow over the networks. We contribute the design and implementation of a visual analytics system, which is called Mobility Viewer, that supports situation-aware understanding and visual reasoning of human mobility. We exemplify our approach with a real citywide data set of seven million users in two months.

Summary Biomass, for example, energy crops, forests, and agricultural residues, has emerged as a renewable energy option to alleviate the consumption of limited fossil fuel resources and the consequent environmental issues. Designing an effective and efficient biomass‐to‐biofuel supply chain involves sophisticated decision‐making processes, often requiring collaborative work on data integration, model specification, scenario analysis, and coordinated implementation and management. To establish an integrated system for such work, challenges exist in (1) the limited interoperability between bioenergy models and geographic information systems (GIS); (2) interactive scenario construction, evaluation, and sharing; and (3) complex optimization problem solving that requires advanced cyberinfrastructure resources to support interactive decision‐making. To resolve these challenges, this paper describes CyberGIS‐BioScope, an interactive and collaborative cyberGIS‐based spatial decision‐making environment for biomass‐to‐biofuel supply chain optimization. The CyberGIS‐BioScope takes advantage of cyberGIS capabilities to process and analyze spatial data and enhance visualization and sharing of optimization results. Meanwhile, the integrated environment makes the complex optimization model and advanced cyberinfrastructure resources easily accessible for agricultural scientists and decision‐makers and thus accelerates their scientific discovery and decision‐making processes. Copyright © 2015 John Wiley & Sons, Ltd.