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IJGI
Special Issues
Geospatial Big Data and Machine Learning Opportunities and Prospects
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Geospatial Big Data and Machine Learning Opportunities and Prospects

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 12014

Special Issue Editors

Dr. Raja Sengupta

E-Mail Website
Guest Editor
Department of Geography and School of Environment, McGill University, 805 Sherbrooke St W., Montreal, QC H3A 0B9, Canada
Interests: agent-based models (ABMs); sensor networks; spatial decision support systems; machine learning of movement behaviors
Special Issues, Collections and Topics in MDPI journals
Dr. Liliana Perez

E-Mail Website
Co-Guest Editor
Laboratory of Environmental Geosimulation (LEDGE), Department of Geography, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montreal, QC H2V 0B3, Canada
Interests: complex systems; Agent-Based modeling; GIS; GIScience; artificial intelligence; machine learning; landscape ecology; forestry; spatial analysis

Special Issue Information

Dear Colleagues,

The increasing availability of large, dynamic data sets creates tremendous opportunities and challenges for empirical science. As an editorial in Nature pointed out, “Big Data” is relevant not only because it is big, but it is also complex. The analysis and use of such data is beyond the comprehension of most individuals using traditional tools. New and innovative methods are required to usefully utilize the torrent of information available to scientists today. Moreover, there are many suggestions that prove that many forms of Big Data have a spatial component (e.g., GPS data). This is particularly true when the information is gathered from spatially distributed sensors connected to the internet and communicating with one another, also referred to as the “Internet of Things”.

Further, the growth in Big Data has been accompanied by new computational methods that include the use of “machine learning” methodologies to process and make sense of such large datasets. Machine learning algorithms can be applied to geospatial Big Data for a variety of reasons, including enhancing our understanding of causal dynamics in systems, capturing those processes, and predicting system states. Although much of current geospatial research relies on simple models with relatively little data assimilation, the emerging intermarriage of geospatial Big Data and machine learning seeks to represent real systems with some fidelity, and can carry significant data and computational demands. The above changes in the computational landscape present both an opportunity and a challenge for the next generation of GIScience research, with some scholars already engaged in exploratory research with this new frontier. Better integration of geospatial Big Data with machine learning algorithms presents opportunities to scale geospatial data analysis over larger geographic extents, represent dynamic system behaviors in near real-time, and use model predictions to anticipate and control networked devices and sensors.

Dr. Raja Sengupta
Dr. Liliana Perez
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

  • geospatial big data
  • machine learning
  • exploratory data analysis
  • classification and regression trees
  • deep learning
  • neural networks
  • self-organizing maps

Published Papers (4 papers)

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Research

15 pages, 3198 KiB  
Article
Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron
by Emily Evenden and Robert Gilmore Pontius Jr
ISPRS Int. J. Geo-Inf. 2021, 10(10), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100686 - 12 Oct 2021
Cited by 1 | Viewed by 2653
Abstract
The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s [...] Read more.
The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s Land Change Modeler proposes encoding a categorical variable onto the continuous closed interval from 0 to 1 based on each category’s Population Evidence Likelihood (PEL) for input to the Multi-Layer Perceptron, which is a type of neural network. We designed examples to test the wisdom of these encodings. The results show that encoding a categorical variable based on each category’s Sample Empirical Probability (SEP) produces results similar to binary encoding and superior to PEL encoding. The Multi-Layer Perceptron’s sigmoidal smoothing function can cause PEL encoding to produce nonsensical results, while SEP encoding produces straightforward results. We reveal the encoding methods by illustrating how a dependent variable gains across an independent variable that has four categories. The results show that PEL can differ substantially from SEP in ways that have important implications for practical extrapolations. If users must encode a categorical variable for input to a neural network, then we recommend SEP encoding, because SEP efficiently produces outputs that make sense. Full article
(This article belongs to the Special Issue Geospatial Big Data and Machine Learning Opportunities and Prospects)
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15 pages, 9590 KiB  
Article
Machine Learning Reveals a Significant Shift in Water Regime Types Due to Projected Climate Change
by Georgy Ayzel
ISPRS Int. J. Geo-Inf. 2021, 10(10), 660; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100660 - 30 Sep 2021
Cited by 3 | Viewed by 1461
Abstract
A water regime type is a cumulative representation of seasonal runoff variability in a textual, qualitative, or quantitative form developed for a particular period. The assessment of the respective water regime type changes is of high importance for local communities and water management [...] Read more.
A water regime type is a cumulative representation of seasonal runoff variability in a textual, qualitative, or quantitative form developed for a particular period. The assessment of the respective water regime type changes is of high importance for local communities and water management authorities, increasing their awareness and opening strategies for adaptation. In the presented study, we trained a machine learning model—the Random Forest classifier—to predict water regime types in northwest Russia based on monthly climatological hydrographs derived for a historical period (1979–1991). Evaluation results show the high efficiency of the trained model with an accuracy of 91.6%. Then, the Random Forest model was used to predict water regime types based on runoff projections for the end of the 21st century (2087–2099) forced by four different General Circulation Models (GCM) and three Representative Concentration Pathway scenarios (RCP). Results indicate that climate is expected to modify water regime types remarkably. There are two primary directions of projected changes. First, we detect the tendency towards less stable summer and winter flows. The second direction is towards a shift in spring flood characteristics. While spring flooding is expected to remain the dominant phase of the water regime, the flood peak is expected to shift towards earlier occurrence and lower magnitude. We identified that the projected changes in water regime types are more pronounced in more aggressive RCP scenarios. Full article
(This article belongs to the Special Issue Geospatial Big Data and Machine Learning Opportunities and Prospects)
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15 pages, 2358 KiB  
Article
Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets
by Ahram Song and Yongil Kim
ISPRS Int. J. Geo-Inf. 2020, 9(10), 601; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100601 - 12 Oct 2020
Cited by 15 | Viewed by 4548
Abstract
Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To [...] Read more.
Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets. Full article
(This article belongs to the Special Issue Geospatial Big Data and Machine Learning Opportunities and Prospects)
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15 pages, 3343 KiB  
Article
CostNet: A Concise Overpass Spatiotemporal Network for Predictive Learning
by Fengzhen Sun, Shaojie Li, Shaohua Wang, Qingjun Liu and Lixin Zhou
ISPRS Int. J. Geo-Inf. 2020, 9(4), 209; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040209 - 30 Mar 2020
Cited by 5 | Viewed by 2343
Abstract
Predicting the futures from previous spatiotemporal data remains a challenging topic. There have been many previous works on predictive learning. However, mainstream models suffer from huge memory usage or the gradient vanishing problem. Enlightened by the idea from the resnet, we propose CostNet, [...] Read more.
Predicting the futures from previous spatiotemporal data remains a challenging topic. There have been many previous works on predictive learning. However, mainstream models suffer from huge memory usage or the gradient vanishing problem. Enlightened by the idea from the resnet, we propose CostNet, a novel recursive neural network (RNN)-based network, which has a horizontal and vertical cross-connection. The core of this network is a concise unit, named Horizon LSTM with a fast gradient transmission channel, which can extract spatial and temporal representations effectively to alleviate the gradient propagation difficulty. In the vertical direction outside of the unit, we add overpass connections from unit output to the bottom layer, which can capture the short-term dynamics to generate precise predictions. Our model achieves better prediction results on moving-mnist and radar datasets than the state-of-the-art models. Full article
(This article belongs to the Special Issue Geospatial Big Data and Machine Learning Opportunities and Prospects)
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