GIS

Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database ...

An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this paper we study a number of flow-query related problems: given a terrain Σ represented as a ...

The visualization of spatio-temporal data helps researchers understand global processes such as animal migration. In particular, interactively restricting the data to different time windows reveals new insights into the short-term and long-term changes ...

Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives and/or constraint functions. These are mostly combinatorial problems (NP-hard) due to the presence of discrete spatial units. ...

We give necessary and sufficient criteria for elementary operations in a two-dimensional terrain to preserve the persistent homology induced by the height function. These operations are edge flips and removals of interior vertices, re-triangulating the ...

Integration of information on road transverse geometric features such as cross slope and superelevation in digital maps can widen the scope of its applications, which is primarily navigation, by enabling driving safety and efficiency applications such ...

Given a spatial graph and a set of node attributes, the Node-attributed Spatial Graph Partitioning (NSGP) problem partitions a node-attributed spatial graph into k homogeneous sub-graphs that minimize both the total RMSErank1 and edge-cuts while meeting ...

Geospatial data integration combines two or more data layers to facilitate advanced querying, analysis, reasoning, and visualization. In general, different layers (e.g., ZIP codes, census blocks, school districts, and land use parcels) have different ...

The wide spread of GPS-enabled devices and the Internet of Things (IoT) has increased the amount of spatial data being generated every second. The current scale of spatial data cannot be handled using centralized systems. This has led to the development ...

Nowadays, the collection of moving object data is significantly increasing due to the ubiquity of GPS-enabled devices. Managing and analyzing this kind of data is crucial in many application domains, including social mobility, pandemics, and ...

Due to the massively increasing amount of available geospatial data and the need to present it in an understandable way, clustering this data is more important than ever. As clusters might contain a large number of objects, having a representative for ...

Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social ...

Although large-scale 3D reconstruction by photogrammetry has been well studied and applied, the reconstruction of night scenery in urban areas has not been thoroughly considered. At night, low-light conditions often cause the images to lack sharpness ...

Fine population distribution both in space and in time is crucial for epidemic management, disaster prevention, urban planning and more. Human mobility data have a great potential for mapping population distribution at a high level of spatiotemporal ...

In this paper, we solve the ambulance dispatch problem with a reinforcement learning oriented strategy. The ambulance dispatch problem is defined as deciding which ambulance to pick up which patient. Traditional studies on ambulance dispatch mainly ...

User geocoding queries in map applications often contain noisy tokens such as typos in street, city name, wrong postal code, redundant words due to copy-paste action, etc. This issue becomes worse with the rapid growth of mobile devices, where errors ...

In telecommunication networks, microwave backhaul links are often used as wireless connections between towers. They are used in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases ...

Traffic prediction is a challenging task due to the time-varying nature of traffic patterns and the complex spatial dependency of road networks. Adding to the challenge, there are a number of errors introduced in traffic sensor reporting, including bias ...

Spatio-textual similarity join is an operation for finding documents, which are both spatially close and textually relevant. Joins in databases are considered to be the most expensive operation; similarly spatio-textual similarity join is a resource ...

The growing demand for high-speed networks is increasing the use of high-frequency electromagnetic waves in wireless networks, including in microwave backhaul links and 5G. The relative higher frequency provides a high bandwidth, but it is very ...

We present DiSA, a Display-driven Spatial Analysis framework for interactive analysis of large-scale geographical vector data. DiSA calculates visualization of analysis results directly using a parallel per-pixel approach with efficient fine-grained ...

The ever rising volume of geospatial data is undeniable. So is the need to explore and analyze these datasets. However, these datasets vary widely in their size, coverage, and accuracy. Therefore, users need to assess these aspects of the data to choose ...

In mobile crowdsourcing, workers are financially motivated to perform self-selected tasks to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to scale for massive tasks and large geographic ...

One of the main challenges in investigation in the field of spatiotemporal databases is that there are few datasets available, they represent specific phenomena, in general, have a small number of observations, and do not provide a ground truth.

In this ...

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 ...

Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we ...

Map matching has long been a fundamental yet challenging problem. However, there are currently only a few public small-scale map matching benchmark datasets. Both the GPS trajectories and the road network in the existing map matching datasets are ...

Forecasting anomalies in urban areas is of great importance for the safety of people. In this paper, we propose Supervised-CityProphet (SCP), an anomaly score matching-based method towards accurate prediction of anomalous crowds. We re-formulate ...

Route reconstruction targets at recovering the actual routes of objects moving on an underlying road network from their times-tamped position measurements. This fundamental pre-processing step to many location-based applications has been extensively ...

The continuous k-Nearest Neighbor queries over spatial-textual data streams (abbr. CkQST) retrieve and continuously monitor at most k nearest neighbor (abbr. kNN) objects to the user-specified location containing all the user-specified keywords, which ...