Special Issue "Deep Learning and Computer Vision for GeoInformation Sciences"

Special Issue Editors

Guest Editor
Dr. James Haworth Website E-Mail
UCL, Department of Civil, Environmental and Geomatic Engineering, London, United Kingdom
Interests: GIScience; machine learning; artificial intelligence; computer vision; transport; geocomputation; geosimulation
Guest Editor
Prof. Suzana Dragicevic Website E-Mail
Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, Burnaby, Canada
Interests: geographic information systems and science (GIS); geosimulation; geographic automata modeling; artificial intelligence; soft computing; geocomputation
Guest Editor
Dr. Marguerite Madden Website E-Mail
Center for Geospatial Research, Department of Geography, University of Georgia, USA
Fax: +1-706-542-2388
Interests: GIScience; landscape ecology; human–environment interactions
Guest Editor
Dr. Mingshu Wang Website E-Mail
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Interests: GIScience; Geodata Science; Urban Informatics
Guest Editor
Dr. Haosheng Huang Website E-Mail
Geographic Information Science (GIS), Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
Interests: GIScience; Location Based Services; Geospatial big data analytics

Special Issue Information

Dear Colleagues,

In recent years, significant progress has been made in the combined fields of deep learning (DL) and computer vision (CV), with applications ranging from driverless cars to facial recognition to robotics. There is a natural synergy between the geoinformation sciences and DL and CV due to the vast quantities of geolocated and time-stamped data being generated from various sources, including satellite imagery, street-level images, and video and airborne and unmanned aerial system (UAS) imagery, as well as social media data, text data, and other data streams. In the field of remote sensing, in particular, significant progress has already been made in object detection, image classification and scene classification, amongst others. More recently, DL architectures have been applied to heterogeneous geospatial data types, such as networks, broadening their applicability across a range of spatial processes.

This Special Issue aims to collate the state of the art in deep learning and computer vision for the geoinformation sciences, from the application of existing DL and CV algorithms in diverse contexts to the development of novel techniques. Submissions are invited across a range of topics related to DL and CV, including but not limited to:

Theory and algorithms: Development of novel theory and algorithms specific to the geoinformation sciences, including methods for modelling heterogeneous spatio-temporal data types.
Integration of DL and CV into traditional modelling frameworks: Using DL and CV to augment traditional modelling techniques, e.g. through data creation, fusion or integrated algorithmic design.
Deep reinforcement learning: Application of deep reinforcement learning to spatial processes.
Geocomputation for DL and CV: Improving the performance and scalability of DL and CV using geocomputational techniques.
Incorporating DL and CV in Geoinformation Science Curricula: Meeting demands in education for incorporating artificial intelligence into educational curricula, particularly geolocational aspects of DL and CV in GIScience programs as well as other disciplines of DL/CV development (e.g., engineering, computer science) and application areas (listed below).
Applications: Open scope within the geoinformation sciences (e.g., transport and mobility, smart cities, agriculture, marine science, ecology, geology, forestry, public health, urban/rural planning, infrastructure, disaster management, social networks, local/global modelling, climate and atmosphere, etc.).

Dr. James Haworth
Dr. Suzana Dragicevic
Dr. Marguerite Madden
Dr. Mingshu Wang
Dr. Haosheng Huang
Guest Editor

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

  • Computer Vision
  • Deep Learning
  • Convolutional and Recurrent Neural Networks
  • Image Classification
  • Object Detection
  • Spatiotemporal
  • Urban sensing and urban computing

Published Papers (6 papers)

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Research

Open AccessArticle
Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective
ISPRS Int. J. Geo-Inf. 2019, 8(9), 417; https://doi.org/10.3390/ijgi8090417 - 17 Sep 2019
Abstract
Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will [...] Read more.
Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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Open AccessArticle
Image Retrieval Based on Learning to Rank and Multiple Loss
ISPRS Int. J. Geo-Inf. 2019, 8(9), 393; https://doi.org/10.3390/ijgi8090393 - 04 Sep 2019
Abstract
Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may [...] Read more.
Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may impede the accuracy of image retrieval. First, when learning the similarity of negative examples, current methods separate negative pairs into equal distance in the embedding space. Thus, the intraclass data distribution might be missed. Second, given a query, either a fraction of data points, or all of them, are incorporated to build up the similarity structure, which makes it rather complex to calculate similarity or to choose example pairs. In this study, in order to achieve more accurate image retrieval, we proposed a method based on learning to rank and multiple loss (LRML). To address the first problem, through learning the ranking sequence, we separate the negative pairs from the query image into different distance. To tackle the second problem, we used a positive example in the gallery and negative sets from the bottom five ranked by similarity, thereby enhancing training efficiency. Our significant experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmarks. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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Open AccessArticle
Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2019, 8(9), 390; https://doi.org/10.3390/ijgi8090390 - 04 Sep 2019
Abstract
Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on [...] Read more.
Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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Open AccessArticle
Using Intelligent Clustering to Implement Geometric Computation for Electoral Districting
ISPRS Int. J. Geo-Inf. 2019, 8(9), 369; https://doi.org/10.3390/ijgi8090369 - 23 Aug 2019
Abstract
Traditional electoral districting is mostly carried out by artificial division. It is not only time-consuming and labor-intensive, but it is also difficult to maintain the principles of fairness and consistency. Due to specific political interests, objectivity is usually distorted and controversial in a [...] Read more.
Traditional electoral districting is mostly carried out by artificial division. It is not only time-consuming and labor-intensive, but it is also difficult to maintain the principles of fairness and consistency. Due to specific political interests, objectivity is usually distorted and controversial in a proxy-election. In order to reflect the spirit of democracy, this study uses computing technologies to automatically divide the constituency and use the concepts of “intelligent clustering” and “extreme arrangement” to conquer many shortcomings of traditional artificial division. In addition, various informational technologies are integrated to obtain the most feasible solutions within the maximum capabilities of the computing system, yet without sacrificing the global representation of the solutions. We take Changhua County, Taiwan as an example of complete electoral districting, and find better results relative to the official version, which obtained a smaller difference in the population of each constituency, more complete and symmetrical constituencies, and fewer regional controversies. Our results demonstrate that multidimensional algorithms using a geographic information system could solve many problems of block districting to make decisions based on different needs. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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Open AccessArticle
Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network
ISPRS Int. J. Geo-Inf. 2019, 8(9), 366; https://doi.org/10.3390/ijgi8090366 - 22 Aug 2019
Abstract
The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In [...] Read more.
The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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Open AccessArticle
Speed Estimation of Multiple Moving Objects from a Moving UAV Platform
ISPRS Int. J. Geo-Inf. 2019, 8(6), 259; https://doi.org/10.3390/ijgi8060259 - 31 May 2019
Abstract
Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and [...] Read more.
Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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