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GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 38988

Special Issue Editor

Prof. Dr. Yosoon Choi

E-Mail Website
Guest Editor
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: smart mining; renewables in mining; space mining; AICBM (AI, IoT, cloud, big data, mobile) convergence; unmanned aerial vehicle; mine planning and design; open-pit mining operation; mine safety; geographic information systems; 3D geo-modeling; geostatistics; hydrological analysis; energy analysis and simulation; design of solar energy conversion systems; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of Artificial Intelligence (AI), including machine learning and deep learning, has developed rapidly in recent years and has been spreading across all industries so that innovative changes are taking place in the industrial field through the combination of AI and domain knowledge.  

This change is also having a significant impact on geographic information systems (GIS). Machine learning has been a core component of spatial analysis in GIS for classification, clustering, and prediction. In addition, deep learning is being integrated with GIS to automatically extract useful information from satellite, aerial or drone imagery by means of image classification, object detection, semantic and instance segmentation, etc. The integration of AI, machine learning, and deep learning with GIS has developed into the concept of a “Geospatial Artificial Intelligence (GeoAI)” that is a new paradigm for geographic knowledge discovery and beyond.

This Special Issue (SI) encourages scientists, engineers, educators, students, and researchers to address the current state-of-the-art in GeoAI. Original research contributions and reviews providing examples of AI, machine learning, and deep learning techniques used in GIS can be included in this SI.

Prof. Dr. Yosoon Choi
Guest Editor

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Keywords

  • Geospatial artificial intelligence
  • Machine learning in GIS
  • Deep learning in GIS
  • Classification
  • Clustering
  • Spatial prediction
  • Spatial interpolation
  • Object detection
  • Semantic segmentation
  • Instance segmentation

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Published Papers (7 papers)

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Editorial

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2 pages, 159 KiB  
Editorial
GeoAI: Integration of Artificial Intelligence, Machine Learning, and Deep Learning with GIS
by Yosoon Choi
Appl. Sci. 2023, 13(6), 3895; https://doi.org/10.3390/app13063895 - 19 Mar 2023
Cited by 18 | Viewed by 10789
Abstract
Geographic Information Systems (GIS) have become increasingly important in various fields such as urban planning, environmental management, transportation, and agriculture [...] Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)

Research

Jump to: Editorial

26 pages, 12898 KiB  
Article
A GIS-Based Approach for Primary Substations Siting and Timing Based on Voronoi Diagram and Particle Swarm Optimization Method
by Alessandro Bosisio, Alberto Berizzi, Marco Merlo, Andrea Morotti and Gaetano Iannarelli
Appl. Sci. 2022, 12(12), 6008; https://doi.org/10.3390/app12126008 - 13 Jun 2022
Cited by 8 | Viewed by 2782
Abstract
The paper aims to provide primary substations’ optimal siting and timing to expand existing distribution networks. The proposed methodology relies on three main features: a geographic information system for capturing, elaborating, and displaying spatial input data; a particle swarm optimization algorithm to locate [...] Read more.
The paper aims to provide primary substations’ optimal siting and timing to expand existing distribution networks. The proposed methodology relies on three main features: a geographic information system for capturing, elaborating, and displaying spatial input data; a particle swarm optimization algorithm to locate and timing the new primary substations; a Voronoi diagram-based approach to find the primary substation service areas and loading. The optimization criteria follow the approach of serving every customer from the nearest primary substation to ensure that the distribution delivery distance is as short as possible, reducing feeders’ cost, electric losses, and service interruption exposure. The algorithm also considers the primary substation transformers’ capacity limit. Thanks to Unareti, the distribution system operator of Milan and Brescia, the methodology was tested by carrying out several simulations, progressively increasing the number of new primary substations. The results obtained confirm the proposed approach’s effectiveness and show that the methodology is a valuable tool to guide Unareti, and distribution system operators in general, in expanding distribution networks to face the challenges of the energy transition. Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)
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12 pages, 2846 KiB  
Article
Remote Sensing Scene Data Generation Using Element Geometric Transformation and GAN-Based Texture Synthesis
by Zhaoyang Liu, Renxiang Guan, Jingyu Hu, Weitao Chen and Xianju Li
Appl. Sci. 2022, 12(8), 3972; https://doi.org/10.3390/app12083972 - 14 Apr 2022
Cited by 8 | Viewed by 2440
Abstract
Classification of remote sensing scene image (RSSI) has been broadly applied and has attracted increasing attention. However, scene classification methods based on convolutional neural networks (CNNs) require a large number of manually labeled samples as training data, which is time-consuming and costly. Therefore, [...] Read more.
Classification of remote sensing scene image (RSSI) has been broadly applied and has attracted increasing attention. However, scene classification methods based on convolutional neural networks (CNNs) require a large number of manually labeled samples as training data, which is time-consuming and costly. Therefore, generating labeled data becomes a practical approach. However, conventional scene generation based on generative adversarial networks (GANs) involve some significant limitations, such as distortion and limited size. To solve the mentioned problems, herein, we propose a method of RSSI generation using element geometric transformation and GAN-based texture synthesis. Firstly, we segment the RSSI, extracting the element information in the RSSI. Then, we perform geometric transformations on the elements and extract the texture information in them. After that, we use the GAN-based method to model and generate the texture. Finally, we fuse the transformed elements with the generated texture to obtain the generated RSSI. The geometric transformation increases the complexity of the scene. The GAN-based texture synthesis ensures the generated scene image is not distorted. Experimental results demonstrate that the RSSI generated by our method achieved a better visual effect than a GAN model. In addition, the performance of CNN classifiers was reduced by 0.44–3.41% on the enhanced data set, which is partly attributed to the complexity of the generated samples. The proposed method was able to generate diverse scene data with sufficient fidelity under conditions of small sample size and solve the accuracy saturation issues of the public scene data sets. Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)
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25 pages, 19094 KiB  
Article
Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
by Nur Atirah Muhadi, Ahmad Fikri Abdullah, Siti Khairunniza Bejo, Muhammad Razif Mahadi and Ana Mijic
Appl. Sci. 2021, 11(20), 9691; https://doi.org/10.3390/app11209691 - 18 Oct 2021
Cited by 46 | Viewed by 8308
Abstract
The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions [...] Read more.
The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities. Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)
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Graphical abstract

21 pages, 4302 KiB  
Article
Comparison of Twelve Machine Learning Regression Methods for Spatial Decomposition of Demographic Data Using Multisource Geospatial Data: An Experiment in Guangzhou City, China
by Guanwei Zhao, Zhitao Li and Muzhuang Yang
Appl. Sci. 2021, 11(20), 9424; https://doi.org/10.3390/app11209424 - 11 Oct 2021
Cited by 5 | Viewed by 2920
Abstract
The spatial decomposition of demographic data at a fine resolution is a classic and crucial problem in the field of geographical information science. The main objective of this study was to compare twelve well-known machine learning regression algorithms for the spatial decomposition of [...] Read more.
The spatial decomposition of demographic data at a fine resolution is a classic and crucial problem in the field of geographical information science. The main objective of this study was to compare twelve well-known machine learning regression algorithms for the spatial decomposition of demographic data with multisource geospatial data. Grid search and cross-validation methods were used to ensure that the optimal model parameters were obtained. The results showed that all the global regression algorithms used in the study exhibited acceptable results, besides the ordinary least squares (OLS) algorithm. In addition, the regularization method and the subsetting method were both useful for alleviating overfitting in the OLS model, and the former was better than the latter. The more competitive performance of the nonlinear regression algorithms than the linear regression algorithms implies that the relationship between population density and influence factors is likely to be non-linear. Among the global regression algorithms used in the study, the best results were achieved by the k-nearest neighbors (KNN) regression algorithm. In addition, it was found that multi-sources geospatial data can improve the accuracy of spatial decomposition results significantly, and thus the proposed method in our study can be applied to the study of spatial decomposition in other areas. Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)
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16 pages, 567 KiB  
Article
Towards Robust Representations of Spatial Networks Using Graph Neural Networks
by Chidubem Iddianozie and Gavin McArdle
Appl. Sci. 2021, 11(15), 6918; https://doi.org/10.3390/app11156918 - 27 Jul 2021
Cited by 10 | Viewed by 3339
Abstract
The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks [...] Read more.
The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks. Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)
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16 pages, 8013 KiB  
Article
A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
by Eric Hsueh-Chan Lu and Chia-Yu Liu
Appl. Sci. 2021, 11(11), 4971; https://doi.org/10.3390/app11114971 - 28 May 2021
Cited by 9 | Viewed by 3030
Abstract
The diameter of PM2.5 is less than that of 2.5 μg/m3 particulate matter; PM2.5 is small enough to enter the body through the alveolar microvasculature and has a major impact on human health. Therefore, people are interested in the establishment of air [...] Read more.
The diameter of PM2.5 is less than that of 2.5 μg/m3 particulate matter; PM2.5 is small enough to enter the body through the alveolar microvasculature and has a major impact on human health. Therefore, people are interested in the establishment of air quality monitoring and forecasting. The historical and current air quality indices (AQI) can now be easily obtained from air quality sensors. However, people are more likely to need the PM2.5 forecasting information. Based on the literature, air quality varies because of a variety of factors, such as the meteorology in urban areas. In this paper, a spatial-temporal approach is proposed to forecast PM2.5 for 48 h using temporal and spatial features. From the temporal perspective, it is considered that the AQI in a few hours may be very similar because AQI is continuous. In addition, this research reveals the relationship between weather similarities and PM2.5 similarity. It is found that the more similar the weather is, the more similar the PM2.5 value is. From a spatial perspective, it is also considered that the air quality may be similar to that of the adjacent monitoring stations. Finally, the experimental results, based on AirBox data, show that the proposed approach outperforms the two methods based on well-established measurements in terms of the PM2.5 forecast error. Full article
(This article belongs to the Special Issue GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS)
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