Special Issue "Geospatial Big Data and Machine Learning Opportunities and Prospects"

Special Issue Editors

Assoc. Prof. Dr. Raja Sengupta
Website
Guest Editor
Department of Geography and School of Environment, McGill University, 805 Sherbrooke St W., Montreal, QC H3A0B9 Canada
Interests: agent-based models (ABMs); sensor networks; spatial decision support systems; machine learning of movement behaviors
Assoc. Prof. Liliana Perez
Website
Co-Guest Editor
Université de Montréal, 1375, Avenue Thérèse Lavoie-Roux, Montréal (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.

Assoc. Prof. Dr. Raja Sengupta
Assoc. Prof. 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 papers will be 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 1000 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 (2 papers)

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Research

Open AccessArticle
Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets
ISPRS Int. J. Geo-Inf. 2020, 9(10), 601; https://doi.org/10.3390/ijgi9100601 - 12 Oct 2020
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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Open AccessArticle
CostNet: A Concise Overpass Spatiotemporal Network for Predictive Learning
ISPRS Int. J. Geo-Inf. 2020, 9(4), 209; https://doi.org/10.3390/ijgi9040209 - 30 Mar 2020
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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