Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

Sit, Muhammed; Koylu, Caglar; Demir, İbrahim

doi:10.1080/17538947.2018.1563219

Cited by 79 publications

(42 citation statements)

References 39 publications

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“…Ontologies applied within flooding to incorporate sensor data, although available, are limited [105][106][107][108] and applied in specific scenarios, as noted in a recent systematic review of flooding ontologies [109]. However, the application of deep learning and semantic web in disaster response has been limited, primarily aimed at classification and identification of disaster-related information in social media [110][111][112] or analysing remote sensing [113] and aerial imagery [114]. The use of semantic technologies in smart cities has led to discovering new opportunities such as information discovery, categorisation of events, complex event processing and reasoning for decision making, as the semantic networks provide a powerful way of transforming knowledge into machine-readable content [115].…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

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Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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“…Despite the semantic knowledge used by the first, its results have not shifted, and in some tasks it has even performed worse. More recently, in 2019, Sit et al [37] applied long short-term memory (LSTM) neural networks to the disaster relatedness task, while comparing its results with linear machine learning algorithms, like support vector machines (SVM) and logistic regressions. Although the results have not improved over Burel et al [36], the deep learning approaches have clearly improved on the results achieved by both SVM and logistic regression models.…”

Section: Disaster Classificationmentioning

confidence: 99%

DisKnow: A Social-Driven Disaster Support Knowledge Extraction System

et al. 2020

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This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.

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“…RNNs, such as Long Short-Term Memory (LSTM), are utilized to handle time series data, such as predicting the next locations of trajectories ) and examining the temporal patterns of crops (Sun et al 2018). RNNs are also used for analyzing geotagged tweets and other natural language texts containing geographic information (Mao et al 2018b, Sit et al 2019, Santos et al 2018. Machine learning models, such as hidden Markov model, are integrated with a variety of geospatial applications, such as indoor navigation (Li et al 2017a) and location prediction of financial services (McKenzie and Slind 2019).…”

Section: Summary and Some Other Applications Of Ai In Geographymentioning

confidence: 99%

Artificial Intelligence Approaches

Wright

et al. 2019

GIS&T BoK

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Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions. Definitions Artificial Intelligence:The study and design of machines or computational methods that can perform tasks that normally require human intelligence. 2. GeoAI: An interdisciplinary field of geography and artificial intelligence. Machine Learning:A sub field in AI that relies on statistical methods or numerical optimization techniques to derive models from data without explicitly programming every model parameter or computing step. Deep Learning:A special type of machine learning that leverages multiple layers of nonlinear processing units, or neurons, to learn representations from raw data to achieve the goal of automatic learning for completing various AI tasks.Description/body AI and GeographyArtificial Intelligence (AI) has received tremendous attention in recent years from academia, industry, and the general public. Despite its recent popularity, the field was born back in 1956 at a workshop at Dartmouth College (McCarthy 1956). AI is a broad field from its beginning, and has many different definitions (Russell et al. 2003). Some definitions focus on designing intelligent machines that can act like humans. For example, the famous Turing Test was designed to see if the responses of a machine can be indistinguishable from those of a real person (Turing 1950). Some other definitions focus on designing and developing computational methods to complete tasks that typically require human intelligence, such as recognizing objects from images or understanding the meaning of natural language sentences. This entry is primarily based on the second type of definitions.The development of AI has experienced falls and rises. Following its early optimism in 1960s and 70s, AI research went through the "AI winter" due to the failures of AI methods in addressing real-world problems. The following decades witnessed several other waves of optimism and disappointment. Since the 21th century, and especially after 2010, there has been significant progress in AI research. Three major factors have contributed to this fast advancement of AI: big data, novel algorithms, and immense computational power. The emergence of ubiquitous sensors and user-generated content on the Web

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Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

Cited by 79 publications

References 39 publications

Explainable Artificial Intelligence for Developing Smart Cities Solutions

Explainable Artificial Intelligence for Developing Smart Cities Solutions

DisKnow: A Social-Driven Disaster Support Knowledge Extraction System

Artificial Intelligence Approaches

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