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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 42955

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Dr. Jacinto Estima

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Co-Guest Editor

Faculty of Design, Technology and Communication of Universidade Europeia, Lisbon, Portugal
Interests: geographic information sciences; volunteered geographical information; machine learning

Dr. Bruno Martins

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Guest Editor

Instituto Superior Técnico and INESC-ID, University of Lisbon, Lisbon, Portugal
Interests: geographic text analysis; geographic information sciences; applied data science and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, Artificial Intelligence (AI) and Deep Learning methods in particular, had a transformative impact in fields such as natural language processing or computer vision, significantly advancing the state-of-the-art in problems like parsing natural language, classifying unstructured data, or semantically segmenting contents. These same techniques can also empower a next generation of Geographical Information Systems (GISs), providing the ability to combine spatial analysis with fast and near human-level perception, this way facilitating location-based discovery and analysis of relevant information.

Several recent studies have for instance shown that AI techniques can be used in a variety of geospatial applications, with examples in remote sensing for Earth observation (e.g., segmenting, classifying, downscaling, or fusing ground-level or aerial/satellite imagery), spatial data analysis (e.g., interpolation of geospatial data with generative adversarial networks), or geographical text analysis (e.g., address geo-coding or geo-referencing place references in documents), among many others (e.g., processing historical maps, gazetteer conflation, etc.).

Many of aforementioned applications can already highlight important challenges in AI for processing geospatial data, including small sample sizes to inform supervised learning, relative to the complexity of the problems, the lack of ground truth information, or the high degree of noise and uncertainty. Despite many successful applications, overcoming the aforementioned challenges requires additional developments, so that AI techniques can be more widely and easily applicable to a broader range of geospatial applications.

For this ISPRS IJGI special issue on geoAI, we invite the community of professionals and researchers, interested in AI for geospatial applications and in the intersection of GIScience and AI, to submit their work.

Prof. Jacinto Estima
Prof. Bruno Martins
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 1700 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.

Keywords

AI for Spatial Data Analysis: Geospatial Interpolation

Regression and Classification
Data Generalization and Downscaling
Simulation and Modeling

AI for Volunteered Geographical Information: Analysis of User-Generated Data (e.g., GPS Trajectories, Social Media Posts, etc.) Automated Content Geocoding

Combining User-Generated and Automatically Inferred Data
Handling Spatial Heterogeneity, Data Completeness and Varying Data Quality

AI for Earth Observation Applications: Semantic and Instance Segmentation of Aerial/Satellite imagery

Fusion of Multiple Imagery or Remote Sensing Products
Analysis of (Multi-Spectral) Image Time-Series
Proximate-Sensing and Analysis of Ground-Level Photos
Analysis of LIDAR data

AI for Geographical Text Analysis: Text Geo-Parsing

Remote Sensing Image Captioning
Geospatial Semantics in Natural Language
Geographic Information Retrieval

Other Techniques and Application Areas

Conflation of Geospatial Databases
Processing Historical Map Images
Recommendation and Location-Based Services
Applied Spatial Analysis (e.g., Health Geographics, Urban Analytics, Precision Agriculture, etc.)

Published Papers (11 papers)

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Research

23 pages, 6698 KiB

Open AccessArticle

Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features

by Muhammed Enes Atik, Zaide Duran and Dursun Zafer Seker

ISPRS Int. J. Geo-Inf. 2021, 10(3), 187; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030187 - 21 Mar 2021

Cited by 39 | Viewed by 4719

Abstract

3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significantly complex. In this study, the classification performance of different machine learning algorithms in multiple scales was evaluated. The feature spaces of the points in the point cloud were created using the geometric features generated based on the eigenvalues of the covariance matrix. Eight supervised classification algorithms were tested in four different areas from three datasets (the Dublin City dataset, Vaihingen dataset and Oakland3D dataset). The algorithms were evaluated in terms of overall accuracy, precision, recall, F1 score and process time. The best overall results were obtained for four test areas with different algorithms. Dublin City Area 1 was obtained with Random Forest as 93.12%, Dublin City Area 2 was obtained with a Multilayer Perceptron algorithm as 92.78%, Vaihingen was obtained as 79.71% with Support Vector Machines and Oakland3D with Linear Discriminant Analysis as 97.30%. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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20 pages, 8982 KiB

Open AccessEditor’s ChoiceArticle

Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos

by Youngok Kang, Nahye Cho, Jiyoung Yoon, Soyeon Park and Jiyeon Kim

ISPRS Int. J. Geo-Inf. 2021, 10(3), 137; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030137 - 04 Mar 2021

Cited by 31 | Viewed by 4391

Abstract

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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17 pages, 2363 KiB

Open AccessArticle

DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

by Junqing Huang, Liguo Weng, Bingyu Chen and Min Xia

ISPRS Int. J. Geo-Inf. 2021, 10(3), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030125 - 01 Mar 2021

Cited by 15 | Viewed by 3049

Abstract

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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16 pages, 2787 KiB

Open AccessArticle

Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

by Daoye Zhu, Chengqi Cheng, Weixin Zhai, Yihang Li, Shizhong Li and Bo Chen

ISPRS Int. J. Geo-Inf. 2021, 10(2), 75; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020075 - 13 Feb 2021

Cited by 3 | Viewed by 1833

Abstract

Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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32 pages, 11315 KiB

Open AccessArticle

Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions

by Abhishek V. Potnis, Surya S. Durbha and Rajat C. Shinde

ISPRS Int. J. Geo-Inf. 2021, 10(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010032 - 13 Jan 2021

Cited by 10 | Viewed by 3829

Abstract

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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18 pages, 5764 KiB

Open AccessArticle

Determining Cover Management Factor with Remote Sensing and Spatial Analysis for Improving Long-Term Soil Loss Estimation in Watersheds

by Fuan Tsai, Jhe-Syuan Lai, Kieu Anh Nguyen and Walter Chen

ISPRS Int. J. Geo-Inf. 2021, 10(1), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010019 - 06 Jan 2021

Cited by 11 | Viewed by 3647

Abstract

The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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24 pages, 8391 KiB

Open AccessArticle

An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning

by Zhe Ma, Zhe Liu, Yuanyuan Zhao, Lin Zhang, Diyou Liu, Tianwei Ren, Xiaodong Zhang and Shaoming Li

ISPRS Int. J. Geo-Inf. 2020, 9(11), 648; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110648 - 29 Oct 2020

Cited by 23 | Viewed by 2976

Abstract

The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is consequently costly in terms of time and money. In order to reduce the need for sample size, this paper proposes an unsupervised classification method based on principal components isometric binning (PCIB). In particular, principal component analysis (PCA) dimensionality reduction is applied to the classification features, followed by the division of the top k principal components into equidistant bins. Bins of the same category are subsequently merged as a class label. Multitemporal Gaofen 1 satellite (GF-1) remote sensing images were collected over the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China in order to map crop types in 2016 and 2017. Our proposed method was compared with commonly used classifiers (random forest, K-means and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA)). Results demonstrate PCIB and random forest to have the highest classification accuracies, reaching 82% in 2016 in the southwest of Hulin City. In Luobei County in 2016, the accuracies of PCIB and random forest were determined as 81% and 82%, respectively. It can be concluded that the overall accuracy of our proposed method meets the basic requirements of classification accuracy. Despite exhibiting a lower accuracy than that of random forest, PCIB does not require a large field sample size, thus making it more suitable for large-scale crop mapping. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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26 pages, 3735 KiB

Open AccessArticle

Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data

by Sina Keller, Raoul Gabriel and Johanna Guth

ISPRS Int. J. Geo-Inf. 2020, 9(11), 638; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110638 - 27 Oct 2020

Cited by 8 | Viewed by 2979

Abstract

Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination

R^{2} = 80.43 %

, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. The implementation of our estimation framework for an exemplary dataset is provided on GitHub. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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20 pages, 4068 KiB

Open AccessArticle

Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

by Jinglun Li, Jiapeng Xiu, Zhengqiu Yang and Chen Liu

ISPRS Int. J. Geo-Inf. 2020, 9(10), 571; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100571 - 29 Sep 2020

Cited by 21 | Viewed by 2940

Abstract

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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14 pages, 3165 KiB

Open AccessArticle

Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

by Zemin Han, Yuanyong Dian, Hao Xia, Jingjing Zhou, Yongfeng Jian, Chonghuai Yao, Xiong Wang and Yuan Li

ISPRS Int. J. Geo-Inf. 2020, 9(8), 478; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080478 - 30 Jul 2020

Cited by 28 | Viewed by 3794

Abstract

Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensing imagery is a difficult problem due to heterogeneous spectral values of the same object on the ground. Fully convolutional networks (FCNs) are a state-of-the-art method that has been increasingly used in image segmentation and classification. However, a systematic quantitative comparison of FCNs on high-spatial-multispectral remote imagery was not yet performed. In this paper, we adopted the three FCNs (FCN-8s, Segnet, and Unet) for Gaofen-2 (GF2) satellite imagery classification. Two scenes of GF2 with a total of 3329 polygon samples were used in the study area and a systematic quantitative comparison of FCNs was conducted with red, green, blue (RGB) and RGB+near infrared (NIR) inputs for GF2 satellite imagery. The results showed that: (1) The FCN methods perform well in land cover classification with GF2 imagery, and yet, different FCNs architectures exhibited different results in mapping accuracy. The FCN-8s model performed best among the Segnet and Unet architectures due to the multiscale feature channels in the upsampling stage. Averaged across the models, the overall accuracy (OA) and Kappa coefficient (Kappa) were 5% and 0.06 higher, respectively, in FCN-8s when compared with the other two models. (2) High-spatial-resolution remote sensing imagery with RGB+NIR bands performed better than RGB input at mapping land cover, and yet the advantage was limited; the OA and Kappa only increased an average of 0.4% and 0.01 in the RGB+NIR bands. (3) The GF2 imagery provided an encouraging result in estimating land cover based on the FCN-8s method, which can be exploited for large-scale land cover mapping in the future. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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19 pages, 2694 KiB

Open AccessArticle

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

by Liguo Weng, Yiming Xu, Min Xia, Yonghong Zhang, Jia Liu and Yiqing Xu

ISPRS Int. J. Geo-Inf. 2020, 9(4), 256; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040256 - 18 Apr 2020

Cited by 58 | Viewed by 5400

Abstract

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet. Full article

(This article belongs to the Special Issue Geospatial Artificial Intelligence)

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