Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to the diversity of deployment scenarios and the significant simulation-to-reality discrepancy. In this paper, we propose \emph{AutoPlan}, a new automatic network planning framework by leveraging digital radio twin (DRT) techniques. We derive the DRT by finetuning the parameters of building materials to reduce the sim-to-real discrepancy based on crowdsource real-world user data. Leveraging the DRT, we design a Bayesian optimization based algorithm to optimize the deployment parameters of base stations efficiently. Using the field measurement from Husker-Net, we extensively evaluate \emph{AutoPlan} under various deployment scenarios, in terms of both coverage and capacity. The evaluation results show that \emph{AutoPlan} flexibly adapts to different scenarios and achieves performance comparable to exhaustive search, while requiring less than 2\% of its computation time.

Proactive safety systems aim to mitigate risks by anticipating future potential conflicts between vehicles and enabling early intervention to prevent work zone related crashes. This study presents an infrastructure-enabled proactive work zone safety warning system that leverages a Digital Twin (DT) environment, integrating real-time multi-sensor data, detailed High-Definition (HD) maps, and a trajectory prediction model based on a historical-prediction attention mechanism. Specifically, the contributions of this study are in two folds: (i) developing an infrastructure-enabled vehicle trajectory prediction framework that leverages the Historical Prediction Network (HPNet) model integrated with Lanelet2 high-definition (HD) maps; and (ii) developing a proactive safety warning generation application that incorporates probabilistic conflict modeling and vehicle bounding-box representations. The development and effectiveness demonstration of the presented trajectory prediction model and proactive warning system is conducted using a co-simulation environment integrating Simulation of Urban MObility (SUMO) and CAR Learning to Act (CARLA) simulators. To evaluate the accuracy of predicted trajectories, we use two standard metrics: Joint Average Displacement Error (ADE) and Joint Final Displacement Error (FDE). Our analyses revealed that the infrastructure-enabled HPNet model demonstrates superior performance on the work zone datasets generated from the cosimulation environment, achieving a minimum Joint FDE of 0.3228 meters and a minimum Joint ADE of 0.1327 meters, lower than the benchmarks on the Argoverse (minJointFDE: 1.0986 m, minJointADE: 0.7612 m) and INTERACTION (minJointFDE: 0.8231 m, minJointADE: 0.2548 m) datasets. In addition, our proactive safety warning generation application demonstrates its ability to issue alerts for potential vehicle conflicts.

Accurate typhoon track forecasting is crucial for early system warning and disaster response. While Transformer-based models have demonstrated strong performance in modeling the temporal dynamics of dense trajectories of humans and vehicles in smart cities, they usually lack access to broader contextual knowledge that enhances the forecasting reliability of sparse meteorological trajectories, such as typhoon tracks. To address this challenge, we propose TyphoFormer, a novel framework that incorporates natural language descriptions as auxiliary prompts to improve typhoon trajectory forecasting. For each time step, we use Large Language Model (LLM) to generate concise textual descriptions based on the numerical attributes recorded in the North Atlantic hurricane database. The language descriptions capture high-level meteorological semantics and are embedded as auxiliary special tokens prepended to the numerical time series input. By integrating both textual and sequential information within a unified Transformer encoder, TyphoFormer enables the model to leverage contextual cues that are otherwise inaccessible through numerical features alone. Extensive experiments are conducted on HURDAT2 benchmark, results show that our TyphoFormer consistently outperforms other state-of-the-art baseline methods, particularly under challenging scenarios involving nonlinear path shifts and limited historical observations.

Knowing vegetation type in an area is crucial for several applications, including ecology, land use management, and infrastructure risk assessment. In combination with recent advancements in image processing, remote sensing technology has been used to perform fast vegetation type estimation and reduce the need for intensive and time-consuming field-based surveys. This paper proposes a weakly supervised method based on deep learning to estimate tree species relying on multi-spectral high-resolution satellite images. We tested the approach against noisy labels, which often occur in real-world datasets. We validate our approach for a study area in Norway and in Italy using images taken in different periods of the year. Our method significantly enhances the quality of the available forestry inventory dataset.

Disasters Such As Hurricanes, Earthquakes, Wildfires, Etc. Are Felt Most Acutely At local and regional levels. These events have exposed weaknesses in how well-prepared infrastructure operators are to keep their services and provide resilient responses. Outages and service disruptions are largely due to the inability of the affected city infrastructure (i.e., power grids) to cope with random and dynamic disruptive events, translating into resilience deficiencies. A significant challenge is the lack of data availability, sharing, and analysis for emergency planning, and restoration. The Special Section (SS) on Data-driven Reliable and Resilient Energy System Against Disasters in the IEEE Transactions on Industrial Informatics aims to address the data-driven approaches for power systems and infrastructure reliability and resilience during small- and large-scale extreme weather events or natural disasters. The specific aim is to utilize the advancements in data mining and data processing to minimize catastrophic conditions that affect the quality of critical infrastructure operations, quality of life, and economic activities.

During extreme weather events, such as hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This article proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a covoting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 2018.

Vegetation is one of the primary causes of outages in electricity transmission and distribution networks and represents a significant expense in maintaining a power grid. While LiDAR or multi-view images can be used for detecting vegetation along power lines, such technologies are costly and difficult to acquire to cover widespread electricity networks. This paper proposes a framework for 3D mapping of trees along power lines using monocular high-resolution satellite images. Such type of imagery has become nowadays affordable and easy to acquire. Furthermore, single snapshots can cover a large portion of the grid in high revisiting time. We train and test different state-of-the-art models to map the contextual information from images into a height prediction. We validate our proposed satellite-based framework for an electricity distribution network in the western part of Norway using actual LiDAR data.

Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) where different sensors and devices record the cyber and physical indicators of the city systems. The collected data are used for improving urban life by offering services such as accurate electric load forecasting, and more efficient traffic management. Traditional monitoring for electricity and transportation networks generally do not provide full observability due to their limited coverage as well as high implementation and maintenance costs. For example, continuous traffic data collection is mostly limited to major highways only in big cities, whereas local roadways are usually covered once or twice a year. Also, there are no high-fidelity and real-time electric monitoring systems in all parts of power distribution networks. Combining the limited data from each of the urban systems together (e.g., electricity, transportation, environment, etc.) provides a better picture of the energy flow in a city. Furthermore, a city should be considered as a collection of the layers of tangled infrastructure networks, which connects people, places, and resources. Therefore, the study of traffic or electricity consumption forecasting should go beyond the transportation and electricity networks and merge with each other and even with other city networks such as environmental networks. As such, this article proposes a Bayesian spatiotemporal Gaussian Process model that employs the most informative spatiotemporal interdependency among different interconnected networks (in this case, electricity, transportation, and weather). The proposed load forecasting method is compared with other state-of-the-art methods using real-life data obtained from the City of Tallahassee in Florida. Results show that the proposed Bayesian spatiotemporal Gaussian Process model outperforms state-of-the-art methods.

Urban mobility is a multidimensional characteristic of cities experienced as layers of interconnected infrastructures, places, people, and information. Therefore, the study of networks such as electricity and transportation systems should go beyond an individual network and merge with other networks. This paper proposes the bundled causality engine as a novel information theory-based approach to characterize the causal dependency between flows of electricity and transportation networks. To validate this hypothesis, electricity load forecasting is performed by combining transportation network data with the smart meter data for the City of Tallahassee, FL, USA. The results show a considerable improvement in the short-term load forecasting accuracy at the household level.