Improving Geospatial Big Data Analytics Approaches

The advent of geospatial big data has led to a paradigm shift where most related applications became data driven, and therefore intensive in both data and computation. This revolution has covered most domains, namely the real-time systems such as web search engines, social networks, and tracking systems. These later are linked to the high-velocity feature, which characterizes the dynamism, the fast changing and moving data streams. Therefore, the response time and speed of such queries, along with the space complexity, are among data stream analysis system requirements, which still require improvements using sophisticated algorithms. In this vein, this chapter discusses new approaches that can reduce the complexity and costs in time and space while improving the efficiency and quality of responses of geospatial big data stream analysis to efficiently detect changes over time, conclude, and predict future events.

On the Use of Deep Learning for Geodata Enrichments

Data is the central element of a geographic information system (GIS) and its cost is often high because of the substantial investment that allows its production. However, these data are often restricted to a service or a category of users. This has highlighted the need to propose and optimize the means of enriching spatial information relevant to a larger number of users. In this chapter, a data enrichment approach that integrates recent advances in machine learning; more precisely, the use of deep learning to optimize the enrichment of GDBs is proposed, specifically, during the topic identification phase. The evaluation of the approach was completed showing its performance.

An Integrated Heuristic Approach for the Long-Distance Heterogeneous Vehicle Routing Problem

In this chapter, a single depot, long-distance heterogeneous vehicle routing problem is studied with fixed costs and vehicle-dependent routing costs (LD-HVRPFD). The LD-HVRPFD considers retailers far away from the single depot and hence route durations could exceed a day. Thus, the number of available vehicles changes through the course of the multi-day planning horizon. Moreover, it is typical to encounter time-variant demand from retailers. To solve the LD-HVRPFD, the authors developed an iterative heuristic solution methodology integrated into a programming platform. The solution method consists of decomposing the VRP into sequential daily problems, model building using macro programming, obtaining a solution using a solver, determining the route-vehicle pairs and time durations, and dynamically updating the truck availability for the next day. The method is illustrated using real data from one of the biggest retail companies in the ready-to-wear sector of textile supply chains. The performance of the heuristic optimization procedure based on time and gap restriction criteria is presented.

Towards an Embedding-Based Approach for the Geolocation of Texts and Users on Social Networks

The geolocation task of textual data shared on social networks like Twitter attracts a progressive attention. Since those data are supported by advanced geographic information systems for multipurpose spatial analysis, new trends to extend the paradigm of geolocated data become more emergent. Differently from statistical language models that are widely adopted in prior works, the authors propose a new approach that is adopted to the geolocation of both tweets and users through the application of embedding models. The authors boost the geolocation strategy with a sequential modelling using recurrent neural networks to delimit the importance of words in tweets with respect to contextual information. They evaluate the power of this strategy in order to determine locations of unstructured texts that reflect unlimited user's writing styles. Especially, the authors demonstrate that semantic proprieties and word forms can be effective to geolocate texts without specifying local words or topics' descriptions per region.

High-Level Languages for Geospatial Analysis of Big Data

In recent years, big data has become a major concern for many organizations. An essential component of big data is the spatio-temporal data dimension known as geospatial big data, which designates the application of big data issues to geographic data. One of the major aspects of the (geospatial) big data systems is the data query language (i.e., high-level language) that allows non-technical users to easily interact with these systems. In this chapter, the researchers explore high-level languages focusing in particular on the spatial extensions of Hadoop for geospatial big data queries. Their main objective is to examine three open source and popular implementations of SQL on Hadoop intended for the interrogation of geospatial big data: (1) Pigeon of SpatialHadoop, (2) QLSP of Hadoop-GIS, and (3) ESRI Hive of GIS Tools for Hadoop. Along the same line, the authors present their current research work toward the analysis of geospatial big data.

Machine Learning for Winter Crop Mapping Using High Spatiotemporal Time Series Satellite Imagery

Reliable information on crops is required to improve agriculture management and face food security challenges. The work aims at experimenting different machine learning algorithms to identify major crops using time-series Sentinel-2 data covering the region of Jendouba, Tunisia. This chapter describes the workflow for automatic extraction of “semantic information” using a supervised classification approach, applied on a region characterized by a persistent cloud cover during the winter growing season. The results indicated that SVM outperforms the other classifiers, and the best accuracy was achieved using SVM on MSI spline temporal gap-filled with an overall accuracy of 0.89 and kappa 0.86, and that most of the classifiers are robust to noise caused by clouds coverage and handle the high dimensionality of input time-series except Bayes classifier. MSI time-series provides a slightly better results than NDVI time-series, and it appears relevant to consider spline temporal interpolation instead of linear temporal interpolation because of the continuous cloud coverage.

Smart GIS-Based Water Information System

Water management is one of the important aspects and a matter of concern for the current world. Geographic Information System (GIS) is one of the important and effective tools that can be used for storing, management, and display of spatial data for water resource management. This chapter primarily focusses on water management. Managing of water resources has become a challenging task these days. There are many natural water resources available on Earth, but correct information about these resources is required. This chapter focuses on a collaborative, localized system capable of answering user queries. The system will work on GIS platform. The system will be beneficial for local governments for planning and management purpose by finding suitable location for the water resources by means of a GIS-based tool.

Natural Language for Querying Geographic Databases

The amount of spatio-temporal data is growing as is its potential in improving several fields (such as hazard characterization and human diseases). Meanwhile, several problems have risen and concern specially retrieving, storing, and interpreting spatio-temporal phenomena. In fact, there is a need today to make the exploitation of this flood of information popularized for a wide range of users. Although this is not the case since now, generally managing such data requires specific skills, especially the structured query language (SQL) expertise. To profit a wide range of users from this technology, natural language is to be exploited to bridge the gap between non-expert users and geographic data exploitation. This is the scope of the chapter.

GIS-Based Evolutionary Approaches Using Multiple-Criteria Decision Analysis for Spatial Issues

Geographic information systems (GIS) have been considered as good decision support tools to provide the decision maker (DM). However, their spatial data functionalities fail to provide any report about the potentials of the information and cannot make rational choice between conflicting alternatives. Literature review shows that the integration of GIS with multiple-criteria decision analysis (MCDA) makes GIS more robust in decision making process. While MCDA are used to support DMs to deal and solve spatial multi-objective optimisation problems (SMOPs), the use of their methods are suited for eliciting the preferences of small group of stakeholders. Unlike to MCDA, Multi-Objective Evolutionary Algorithms (MOEA) perform well on solving SMOPS conflicting objectives since only one iteration of the algorithm gives rise to a set of trade-off solutions. However, only choosing better compromise doesn't completely solve the problem. Recently, a growing interest in combining MCDA and MOEA techniques has been seen. The chapter approaches the idea of integration of GIS, MOEA, and MCDA to solve SMOP.

Investigating the Pixel Quality Influence on Forecasting Vegetation Change Dynamics

To date, analysis of remotely sensed images remains a big challenge. Despite its high quality and free availability, scientists ask more questions about the reliability of the existent works and developed tools. Indeed, the input choice is under investigation in order to minimize the imprecision within the work's methodology and results. In order to construct a good forecasting model, the researcher focuses on the first place on the data collection. Traditionally, this step is usually neglected, or it does not attract a sufficient amount of attention. Therefore, the obtained forecaster may be trained on the false data sets which makes more questions about its reliability. This chapter investigates the influence of the presence of mixed pixel on the forecasting accuracy final results of vegetation dynamics tracking. The authors also use different similarity measures to differentiate between the pure and the mixed time series.