Special Issue "Artificial Intelligence for Multisource Geospatial Information"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 11536

Special Issue Editors

Dr. Gloria Bordogna
E-Mail Website
Guest Editor
CNR IREA, via Bassini 15, 20133 Milano, Italy
Interests: fuzzy logic and soft computing for the representation and management of imprecision and uncertainty of textual and geographic information; volunteered geographic information user-driven quality assessment in citizen science; crowdsourced information spatiotemporal analytics; information retrieval on the web; flexible query languages for information retrieval and geographic information systems; ill-defined environmental knowledge representation and management; multisource geographic information fusion and synthesis
Special Issues, Collections and Topics in MDPI journals
Dr. Cristiano Fugazza
E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR-IREA), via Bassini 15, I20133 Milan, Italy
Interests: spatial data infrastructures; semantic web and XML technologies; artificial intelligence

Special Issue Information

Dear Colleague,

This Special Issue is collecting original research contributions focused on the definition and application of artificial intelligence methods for acquisition, filtering, management, analysis, discovery, and visualization of geospatial information from multiple sources, i.e., geospatial big data: open geospatial data, remote sensing images and derived products, IoT georeferenced data, crowdsourced geotagged information from social networks, and volunteered geographic information. Key applications are Earth observation for territorial monitoring and change detections, including urban and land-use dynamics and anomaly detection; social condition and habit detection, characterization, monitoring, and prediction regarding, for example, poverty locations, citizens mobility, work, and recreation; event detection and forecasting for emergency preparedness and management, and many more.

This field, also referred to as geospatial artificial intelligence (or geoAI), applies many techniques of the most general artificial intelligence (AI), such as machine learning, deep learning, knowledge discovery, data mining, and soft computing, and semantic representation and analysis. In fact, recent studies aim to bridge the gap between most of these technologies and more traditional approaches to knowledge management; thus, easing advanced features such as explainable AI. However, the specificities and importance of the geospatial dimension, its heterogeneity in terms of both conceptualization (place versus space) and scales, the need for representing distinct semantics of locations, as well as analyzing the role of their temporal changes by performing geospatial and temporal reasoning pose new challenges and opportunities that AI has to face.

Topics of interest include but are not limited to the following:

  • Definition of novel methods for temporal mining of geospatial events so as to be able to identify their latency period in specific areas, possible periodicity, and contributing physical and environmental factors. This can exploit VGI and social network information, remote sensing derived proxies, and historic geospatial data;
  • Geo big data synthesis and reduction approaches modeling uncertainty and different reliability of the sources, the accuracy of the geospatial data, its incompleteness, complementarity, redundancy, possible inconsistency, and conflicts;
  • Enhancement of low spatiotemporal and spectral resolution imagery by new operators of multiple source fusion to improve feature and image recognition;
  • Geospatial data science methods for image interpretation;
  • Visual-based database creation that encapsulates spatial data and location information: for example, by automatic interpretation of cartographic maps through the separation of text (such as names of places) from map features, for automatic feature naming; by automated data capture and extraction of words or other information from photographs to describe points of interest;
  • Mobile application design for real-time suggestions of information about places exploiting social networks and web contents;
  • Augmented reality methods for improving real time geospatiotemporal data visualization and navigation, for example, to manage crowded spaces;
  • Knowledge graphs and ontologies for geospatial data representation and reasoning.

Dr. Gloria Bordogna
Dr. Cristiano Fugazza
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

Article
Soft Integration of Geo-Tagged Data Sets in J-CO-QL+
ISPRS Int. J. Geo-Inf. 2022, 11(9), 484; https://doi.org/10.3390/ijgi11090484 - 13 Sep 2022
Viewed by 415
Abstract
The possibility offered by the current technology to collect and store data sets regarding public places located on the Earth globe is posing new challenges, as far as the integration of these data sets is concerned. Analysts usually need to perform such an [...] Read more.
The possibility offered by the current technology to collect and store data sets regarding public places located on the Earth globe is posing new challenges, as far as the integration of these data sets is concerned. Analysts usually need to perform such an integration from scratch, without performing complex and long preprocessing or data-cleaning tasks, as well as without performing training activities that require tedious and long labeling of data; furthermore, analysts now have to deal with the popular JSON format and with data sets stored within JSON document stores. This paper demonstrates that a methodology based on soft integration (i.e., data integration performed through soft computing and fuzzy sets) can now be effectively applied from scratch, through the J-CO Framework, which is a stand-alone tool devised to process JSON data sets stored within JSON document stores, possibly by performing soft querying on data sets. Specifically, the paper provides the following contributions: (1) It presents a soft-computing technique for integrating data sets describing public places, without any preliminary pre-processing, cleaning and training, which can be applied from scratch; (2) it presents current capabilities for soft integration of JSON data sets, provided by the J-CO Framework; (3) it demonstrates the effectiveness of the soft integration technique; (4) it shows how a stand-alone tool able to support soft computing (as the J-CO Framework) can be effective and efficient in performing data-integration tasks from scratch. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media
ISPRS Int. J. Geo-Inf. 2022, 11(9), 476; https://doi.org/10.3390/ijgi11090476 - 02 Sep 2022
Viewed by 533
Abstract
Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining [...] Read more.
Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining approach to provide in-depth insights related to the COVID-19 pandemic and applied to the Arabic language. We first developed a technique to infer geospatial information from non-geotagged Arabic tweets. Secondly, a sentiment analysis mechanism at various levels of spatial granularities and separate topic scales is introduced. We applied sentiment-based classifications at various location resolutions (regions/countries) and separate topic abstraction levels (subtopics and main topics). In addition, a correlation-based analysis of Arabic tweets and the official health providers’ data will be presented. Moreover, we implemented several mechanisms of topic-based analysis using occurrence-based and statistical correlation approaches. Finally, we conducted a set of experiments and visualized our results based on a combined geo-social dataset, official health records, and lockdown data worldwide. Our results show that the total percentage of location-enabled tweets has increased from 2% to 46% (about 2.5M tweets). A positive correlation between top topics (lockdown and vaccine) and the COVID-19 new cases has also been recorded, while negative feelings of Arab Twitter users were generally raised during this pandemic, on topics related to lockdown, closure, and law enforcement. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering
ISPRS Int. J. Geo-Inf. 2022, 11(4), 245; https://doi.org/10.3390/ijgi11040245 - 10 Apr 2022
Cited by 4 | Viewed by 1021
Abstract
With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making [...] Read more.
With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making process of tourists. Among UGCs, photos represent tourists’ visual preferences for a specific area. Paying attention to the value of photos, several studies have attempted to analyze them using deep learning technology. However, the research methods that analyze tourism photos using recent deep learning technology have a limitation in that they cannot properly classify unique photos appearing in specific tourist attractions with predetermined photo categories such as Places365 or ImageNet dataset or it takes a lot of time and effort to build a separate training dataset to train the model and to generate a tourism photo classification category according to a specific tourist destination. The purpose of this study is to propose a method of automatically classifying tourist photos by tourist attractions by applying the methods of the image feature vector clustering and the deep learning model. To this end, first, we collected photos attached to reviews posted by foreign tourists on TripAdvisor. Second, we embedded individual images as 512-dimensional feature vectors using the VGG16 network pre-trained with Places365 and reduced them to two dimensions with t-SNE(t-Distributed Stochastic Neighbor Embedding). Then, clusters were extracted through HDBSCAN(Hierarchical Clustering and Density-Based Spatial Clustering of Applications with Noise) analysis and set as a regional image category. Finally, the Siamese Network was applied to remove noise photos within the cluster and classify photos according to the category. In addition, this study attempts to confirm the validity of the proposed method by applying it to two representative tourist attractions such as ‘Gyeongbokgung Palace’ and ‘Insadong’ in Seoul. As a result, it was possible to identify which visual elements of tourist attractions are attractive to tourists. This method has the advantages in that it is not necessary to create a classification category in advance, it is possible to flexibly extract categories for each tourist destination, and it is able to improve classification performance even with a rather small volume of a dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
The Use of Machine Learning Algorithms in Urban Tree Species Classification
ISPRS Int. J. Geo-Inf. 2022, 11(4), 226; https://doi.org/10.3390/ijgi11040226 - 26 Mar 2022
Cited by 2 | Viewed by 1610
Abstract
Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; [...] Read more.
Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Modeling and Querying Fuzzy SOLAP-Based Framework
ISPRS Int. J. Geo-Inf. 2022, 11(3), 191; https://doi.org/10.3390/ijgi11030191 - 11 Mar 2022
Viewed by 904
Abstract
Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analytical processing (OLAP), developed [...] Read more.
Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analytical processing (OLAP), developed for this purpose, provides appropriate data structures and supports querying multidimensional numeric and alphanumeric data. However, uncertainty and fuzziness are inherent in the data in many complex database applications, especially in spatiotemporal database applications. Therefore, there is always a need to support flexible queries and analyses on uncertain and fuzzy data, due to the nature of the data in these complex spatiotemporal applications. FSOLAP is a new framework based on fuzzy logic technologies and spatial online analytical processing (SOLAP). In this study, we use crisp measures as input for this framework, apply fuzzy operations to obtain the membership functions and fuzzy classes, and then generate fuzzy association rules. Therefore, FSOLAP does not need to use predefined sets of fuzzy inputs. This paper presents the method used to model the FSOLAP and manage various types of complex and fuzzy spatiotemporal queries using the FSOLAP framework. In this context, we describe how to handle non-spatial and fuzzy spatial queries, as well as spatiotemporal fuzzy query types. Additionally, while FSOLAP primarily includes historical data and associated queries and analyses, we also describe how to handle predictive fuzzy spatiotemporal queries, which typically require an inference mechanism. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image
ISPRS Int. J. Geo-Inf. 2022, 11(3), 165; https://doi.org/10.3390/ijgi11030165 - 25 Feb 2022
Cited by 6 | Viewed by 1407
Abstract
Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, [...] Read more.
Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
RepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2022, 11(3), 158; https://doi.org/10.3390/ijgi11030158 - 22 Feb 2022
Viewed by 1092
Abstract
Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a [...] Read more.
Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a backbone feature-extraction network called “RepDarkNet” that adds several convolutional layers to CSPDarkNet53. RepDarkNet considerably improves the overall network accuracy with almost no increase in inference time. In addition, we propose a multi-scale cross-layer detector that significantly improves the capability of the network to detect small targets. Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case. Experiments show that the proposed method dramatically improves detection accuracy, achieving AP = 75.53% for the Dior-vehicle dataset and mAP = 84.3% for the Dior dataset, both of which exceed the state-of-the-art level. Finally, we present a series of improvement strategies that justifies our improvement measures. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City
ISPRS Int. J. Geo-Inf. 2022, 11(3), 151; https://doi.org/10.3390/ijgi11030151 - 22 Feb 2022
Cited by 5 | Viewed by 1253
Abstract
The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. [...] Read more.
The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. Conventional data sources that quantify people on the street and streetscape characteristics, such as questionnaires, field surveys, or manual audits, are labor-intensive, time-consuming, and unable to cover a large area with a sufficient spatial resolution. Emerging cell phone and social media data have been used to measure ambient population, but they cannot distinguish between the street and indoor populations. This study addresses these limitations by combining Baidu Street View (BSV) images, deep learning algorithms, and spatial statistical regression models to examine the influences of people on the street and in the streetscape physical environment on street crime in a large Chinese city. First, we collected fine-grained street view images from the Baidu Map website. Then, we constructed a Faster R-CNN network to detect discrete elements with distinct outlines (such as persons) in each image. From this, we counted the number of people on the street in every BSV image and finally obtained the community-level total amounts. Additionally, the PSPNet network was developed for pixel-wise semantic segmentation to determine the proportions of other streetscape features such as buildings in each BSV image, based on which we obtained their community-level averages. The quantitative measurement of people on the street and a set of streetscape features that had potential influences on crime were finally derived by combining the outputs of two deep learning networks. To account for the spatial autocorrelation effect and distributional characteristics of crime data, we constructed a set of spatial lag negative binomial regression models to investigate how three types of street crime (i.e., total crime, property crime, and violent crime) were affected by the number of people on the street and the streetscape-built conditions. The models also controlled the effect of socioeconomic and demographic factors, land use features, the formal surveillance level, and transportation facilities. The models with people on the street and streetscape environment features had noticeable performance improvements, demonstrating the necessity for accounting for the effect of these factors when understanding street crime. Specifically, the number of people on the street had significantly positive impacts on the total street crime and street property crime. However, no statistically significant impact was found on street violent crime. The average proportions of the paths, buildings, and trees were associated with significantly lower street crime among physical streetscape features. Additionally, the statistical significances of most control variables conformed to previous research findings. This study is the first to combine Street View images and deep learning algorithms to retrieve the number of people on the street and the features of the visual streetscape environment to understand street crime. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Cloud and Snow Segmentation in Satellite Images Using an Encoder–Decoder Deep Convolutional Neural Networks
ISPRS Int. J. Geo-Inf. 2021, 10(7), 462; https://doi.org/10.3390/ijgi10070462 - 06 Jul 2021
Cited by 4 | Viewed by 938
Abstract
The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is proposed. [...] Read more.
The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is proposed. In this method, the atrous spatial pyramid pooling (ASPP) module is used to enhance the encoder, while the decoder is enhanced with the fusion of features from different stages of the encoder, which improves the segmentation accuracy. Comparative experiments show that the proposed method is superior to DeepLabV3+ with Xception and ResNet50. Additionally, a rough-labeled dataset containing 23,520 images and fine-labeled data consisting of 310 images from the TH-1 satellite are created, where we studied the relationship between the quality and quantity of labels and the performance of cloud and snow segmentation. Through experiments on the same network with different datasets, we found that the cloud and snow segmentation performance is related more closely to the quantity of labels rather than their quality. Namely, under the same labeling consumption, using rough-labeled images only performs better than rough-labeled images plus 10% fine-labeled images. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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Article
Implicit, Formal, and Powerful Semantics in Geoinformation
ISPRS Int. J. Geo-Inf. 2021, 10(5), 330; https://doi.org/10.3390/ijgi10050330 - 13 May 2021
Cited by 3 | Viewed by 1048
Abstract
Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art [...] Read more.
Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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